SemViQA/bc-xlmr-viwikifc

SemViQA-BC: Vietnamese Binary Classification for Claim Verification

Model Description

SemViQA-BC is a core component of the SemViQA system, specifically designed for binary classification in Vietnamese fact-checking tasks. This model predicts whether a given claim is SUPPORTED or REFUTED based on retrieved evidence.

Model Information

• Developed by: SemViQA Research Team
• Fine-tuned model: XLM-R
• Supported Language: Vietnamese
• Task: Binary Classification (Fact Verification)
• Dataset: ViWikiFC

SemViQA-BC is one of the key components of the two-step classification (TVC) approach in the SemViQA system. It focuses on binary classification, determining whether a claim is SUPPORTED or REFUTED. This step follows an initial three-class classification, where claims are first categorized as SUPPORTED, REFUTED, or NOT ENOUGH INFORMATION (NEI). By incorporating Cross-Entropy Loss and Focal Loss, SemViQA-BC enhances precision in claim verification, ensuring more accurate fact-checking results

Usage Example

Direct Model Usage

# Install semviqa
!pip install semviqa

# Initalize a pipeline
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer
from semviqa.tvc.model import ClaimModelForClassification

tokenizer = AutoTokenizer.from_pretrained("SemViQA/bc-xlmr-viwikifc")
model = ClaimModelForClassification.from_pretrained("SemViQA/bc-xlmr-viwikifc", num_labels=2)
claim = "Chiến tranh với Campuchia đã kết thúc trước khi Việt Nam thống nhất."
evidence = "Sau khi thống nhất, Việt Nam tiếp tục gặp khó khăn do sự sụp đổ và tan rã của đồng minh Liên Xô cùng Khối phía Đông, các lệnh cấm vận của Hoa Kỳ, chiến tranh với Campuchia, biên giới giáp Trung Quốc và hậu quả của chính sách bao cấp sau nhiều năm áp dụng."

inputs = tokenizer(
    claim,
    evidence,
    truncation="only_second",
    add_special_tokens=True,
    max_length=256,
    padding='max_length',
    return_attention_mask=True,
    return_token_type_ids=False,
    return_tensors='pt',
)

labels = ["SUPPORTED", "REFUTED"]

with torch.no_grad():
    outputs = model(**inputs)

logits = outputs["logits"]
probabilities = F.softmax(logits, dim=1).squeeze()

for i, (label, prob) in enumerate(zip(labels, probabilities.tolist()), start=1):
    print(f"{i}) {label} {prob:.4f}")
# 1) SUPPORTED 0.0028
# 2) REFUTED 0.9972

Evaluation Results

SemViQA-BC achieved impressive results on the test set, demonstrating accurate and efficient classification capabilities. The detailed evaluation of SemViQA-BC is presented in the table below.

Method		ViWikiFC
ER	VC	Strict Acc	VC Acc	ER Acc	Time (s)
TF-IDF	InfoXLMlarge	75.56	82.21	90.15	131
	XLM-Rlarge	76.47	82.78	90.15	134
	Ernie-Mlarge	75.56	81.83	90.15	144
BM25	InfoXLMlarge	70.44	79.01	83.50	130
	XLM-Rlarge	70.97	78.91	83.50	132
	Ernie-Mlarge	70.21	78.29	83.50	141
SBert	InfoXLMlarge	74.99	81.59	89.72	195
	XLM-Rlarge	75.80	82.35	89.72	194
	Ernie-Mlarge	75.13	81.44	89.72	203
QA-based approaches	VC
ViMRClarge	InfoXLMlarge	77.28	81.97	92.49	3778
	XLM-Rlarge	78.29	82.83	92.49	3824
	Ernie-Mlarge	77.38	81.92	92.49	3785
InfoXLMlarge	InfoXLMlarge	78.14	82.07	93.45	4092
	XLM-Rlarge	79.20	83.07	93.45	4096
	Ernie-Mlarge	78.24	82.21	93.45	4102
LLM
Qwen2.5-1.5B-Instruct		51.03	65.18	78.96	7665
Qwen2.5-3B-Instruct		44.38	62.31	71.35	12123
LLM	VC
Qwen2.5-1.5B-Instruct	InfoXLMlarge	66.14	76.47	78.96	7788
	XLM-Rlarge	67.67	78.10	78.96	7789
	Ernie-Mlarge	66.52	76.52	78.96	7794
Qwen2.5-3B-Instruct	InfoXLMlarge	59.88	72.50	71.35	12246
	XLM-Rlarge	60.74	73.08	71.35	12246
	Ernie-Mlarge	60.02	72.21	71.35	12251
SER Faster (ours)	TVC (ours)
TF-IDF + ViMRClarge	Ernie-Mlarge	79.44	82.93	94.60	410
TF-IDF + InfoXLMlarge	Ernie-Mlarge	79.77	83.07	95.03	487
SER (ours)	TVC (ours)
TF-IDF + ViMRClarge	InfoXLMlarge	80.25	83.84	94.69	2731
	XLM-Rlarge	80.34	83.64	94.69	2733
	Ernie-Mlarge	79.53	82.97	94.69	2733
TF-IDF + InfoXLMlarge	InfoXLMlarge	80.68	83.98	95.31	3860
	XLM-Rlarge	80.82	83.88	95.31	3843
	Ernie-Mlarge	80.06	83.17	95.31	3891

Citation

If you use SemViQA-BC in your research, please cite:

@misc{nguyen2025semviqasemanticquestionanswering,
      title={SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking},
      author={Nam V. Nguyen and Dien X. Tran and Thanh T. Tran and Anh T. Hoang and Tai V. Duong and Di T. Le and Phuc-Lu Le},
      year={2025},
      eprint={2503.00955},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.00955},
}

🔗 Paper Link: SemViQA on arXiv
🔗 Source Code: GitHub - SemViQA

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