Dataset size: 7,232 examples.
Shared fixed stratified splits for FC and GE: 5,785 train / 723 development / 724 test.
Labels: Supported, Refuted, and Not Enough Information.
Fine-tuning seeds: [42, 22, 202].
Training: 3 epoch(s), AdamW, learning rate 2e-05, weight decay 0.01, warmup ratio 0.1.
Effective train batch size: 8 (hard-validated against every published run).
Maximum sequence length: 256.
Input mode: raw Vietnamese claim and passage.
Claim and full article context share one token budget; the currently longer sequence is truncated first until the pair fits the encoder limit.
Topic, author, outlet, URL and other source metadata are excluded from model inputs.
No class weighting, resampling, retrieval model, sentence ranking, test-time model selection or external evidence is used.
Checkpoints are selected by development Macro-F1. The representative published checkpoint is seed 42, selected only by development Macro-F1.

Results

Test metrics are reported as mean ± sample standard deviation over seeds [42, 22, 202].

Metric	Mean ± std
Test Macro-F1	0.5631 ± 0.0145
Test accuracy	0.5612 ± 0.0149
Test macro precision	0.5713 ± 0.0159
Test macro recall	0.5599 ± 0.0148
Development Macro-F1	0.5612 ± 0.0084

Per-seed results

seed	dev_macro_f1	test_macro_f1	test_accuracy	micro_batch_size	gradient_accumulation_steps
22.000000	0.563189	0.571691	0.567680	8.000000	1.000000
42.000000	0.568413	0.546268	0.544199	8.000000	1.000000
202.000000	0.551967	0.571242	0.571823	8.000000	1.000000

Label mapping

json

{
  "0": "supported",
  "1": "refuted",
  "2": "not_enough_information"
}

Usage

python

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_id = "BaoNhan/vibert-base-cased-ViFactCheck-FC"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

claim = "Thông tin này đã được cơ quan chức năng xác nhận."
context = "Bài báo cung cấp bằng chứng liên quan đến phát biểu trên."
inputs = tokenizer(
    claim,
    context,
    return_tensors="pt",
    truncation="longest_first",
    max_length=256,
)
with torch.no_grad():
    probabilities = model(**inputs).logits.softmax(dim=-1)[0]
predicted_id = int(probabilities.argmax())
print(model.config.id2label[predicted_id], probabilities.tolist())

Files

aggregate_metrics.json: aggregate metrics and training manifest.
artifacts/per_seed_results.csv: one row per fine-tuning seed.
artifacts/seed_*_confusion_matrix.csv: confusion matrix for each seed.
artifacts/seed_*_classification_report.json: per-class metrics.
artifacts/seed_*_test_predictions.csv: IDs, gold/predicted labels and probabilities; raw claims and passages are excluded.

Limitations

ViFactCheck supplies the correct source article and therefore does not evaluate open-web evidence retrieval. VFC-FC can truncate relevant information in long articles and jointly measures verification plus robustness to irrelevant context. VFC-GE uses oracle gold evidence and must not be presented as a realistic end-to-end deployment setting. This model is a research classifier, not an automated arbiter of truth, and may produce confidently incorrect predictions.

Dataset citation

bibtex

@inproceedings{hoa2025vifactcheck,
  title={ViFactCheck: A New Benchmark Dataset and Methods for Multi-domain News Fact-Checking in Vietnamese},
  author={Hoa, Tran Thai and Duy, Tran Quang and Tran, Khanh Quoc and Nguyen, Kiet Van},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={1},
  pages={308--316},
  year={2025},
  doi={10.1609/aaai.v39i1.32008}
}