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--- |
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language: |
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- vi |
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library_name: transformers |
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license: mit |
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pipeline_tag: text-classification |
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tags: |
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- SemViQA |
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- three-class-classification |
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- fact-checking |
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hf_hub_url: SemViQA/tc-infoxlm-isedsc01 |
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--- |
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# SemViQA-TC: Vietnamese Three-class Classification for Claim Verification |
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## Model Description |
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**SemViQA-TC** is one of the key components of the **SemViQA** system, designed for **three-class classification** in Vietnamese fact-checking. This model classifies a given claim into one of three categories: **SUPPORTED**, **REFUTED**, or **NOT ENOUGH INFORMATION (NEI)** based on retrieved evidence. This model contributes to addressing the growing need for robust fact-checking solutions, particularly for low-resource languages like Vietnamese, where existing methods often struggle with semantic nuances and complex linguistic structures. SemViQA aims to balance precision and speed in fact verification. |
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### **Model Information** |
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- **Developed by:** [SemViQA Research Team](https://huggingface.co/SemViQA) |
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- **Fine-tuned model:** [InfoXLM](https://huggingface.co/microsoft/infoxlm-large) |
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- **Supported Language:** Vietnamese |
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- **Task:** Three-Class Classification (Fact Verification) |
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- **Dataset:** [ISE-DSC01](https://codalab.lisn.upsaclay.fr/competitions/15497) |
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SemViQA-TC serves as the **first step in the two-step classification process** of the SemViQA system. It initially categorizes claims into three classes: **SUPPORTED, REFUTED, or NEI**. For claims classified as **SUPPORTED** or **REFUTED**, a secondary **binary classification model (SemViQA-BC)** further refines the prediction. This hierarchical classification strategy enhances the accuracy of fact verification. This approach aims to achieve state-of-the-art results by combining Semantic-based Evidence Retrieval (SER) and Two-step Verdict Classification (TVC). |
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## Usage Example |
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Direct Model Usage |
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```Python |
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# Install semviqa |
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!pip install semviqa |
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# Initalize a pipeline |
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import torch |
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import torch.nn.functional as F |
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from transformers import AutoTokenizer |
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from semviqa.tvc.model import ClaimModelForClassification |
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tokenizer = AutoTokenizer.from_pretrained("SemViQA/tc-infoxlm-isedsc01") |
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model = ClaimModelForClassification.from_pretrained("SemViQA/tc-infoxlm-isedsc01") |
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claim = "Chiến tranh với Campuchia đã kết thúc trước khi Việt Nam thống nhất." |
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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." |
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inputs = tokenizer( |
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claim, |
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evidence, |
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truncation="only_second", |
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add_special_tokens=True, |
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max_length=256, |
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padding='max_length', |
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return_attention_mask=True, |
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return_token_type_ids=False, |
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return_tensors='pt', |
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) |
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labels = ["NEI", "SUPPORTED", "REFUTED"] |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs["logits"] |
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probabilities = F.softmax(logits, dim=1).squeeze() |
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for i, (label, prob) in enumerate(zip(labels, probabilities.tolist()), start=1): |
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print(f"{i}) {label} {prob:.4f}") |
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# 1) NEI 0.0000 |
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# 2) SUPPORTED 0.0011 |
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# 3) REFUTED 0.9989 |
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``` |
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## **Citation** |
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If you use **SemViQA-TC** in your research, please cite: |
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```bibtex |
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@misc{nguyen2025semviqasemanticquestionanswering, |
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title={SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking}, |
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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}, |
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year={2025}, |
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eprint={2503.00955}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2503.00955}, |
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} |
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``` |
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🔗 **Paper Link:** [SemViQA on arXiv](https://arxiv.org/abs/2503.00955) |
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🔗 **Source Code:** [GitHub - SemViQA](https://github.com/DAVID-NGUYEN-S16/SemViQA) |
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## About |
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*Built by Dien X. Tran* |
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[](https://www.linkedin.com/in/xndien2004/) |
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For more details, visit the project repository. |
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[](https://github.com/DAVID-NGUYEN-S16/SemViQA) |
