Improve model card: Add model abstract and minor formatting improvements

README.md

@@ -2,29 +2,27 @@
 language:
 - vi
 library_name: transformers
+license: mit
+pipeline_tag: text-classification
 tags:
 - SemViQA
 - three-class-classification
 - fact-checking
-pipeline_tag: text-classification
-license: mit
 ---
 
 # SemViQA-TC: Vietnamese Three-class Classification for Claim Verification  
 
 ## Model Description  
 
-**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.
+**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.  It serves as the first step in the SemViQA system's two-step classification process. It initially categorizes claims, and for those classified as SUPPORTED or REFUTED, a secondary binary classification model (SemViQA-BC) further refines the prediction. This hierarchical approach enhances fact-checking accuracy.
 
-### **Model Information**  
+### Model Information  
 - **Developed by:** [SemViQA Research Team](https://huggingface.co/SemViQA)  
 - **Fine-tuned model:** [Ernie-M](https://huggingface.co/MoritzLaurer/ernie-m-large-mnli-xnli)  
 - **Supported Language:** Vietnamese  
 - **Task:** Three-Class Classification (Fact Verification)  
 - **Dataset:** [ViWikiFC](https://arxiv.org/abs/2405.07615)  
 
-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.
-
 ## Usage Example
 
 Direct Model Usage
@@ -70,7 +68,7 @@ for i, (label, prob) in enumerate(zip(labels, probabilities.tolist()), start=1):
 # 3) REFUTED 0.9998
 ```
 
-## **Citation**
+## Citation
 
 If you use **SemViQA-TC** in your research, please cite:
 
@@ -87,11 +85,4 @@ If you use **SemViQA-TC** in your research, please cite:
 ```
 
 🔗 **Paper Link:** [SemViQA on arXiv](https://arxiv.org/abs/2503.00955)  
-🔗 **Source Code:** [GitHub - SemViQA](https://github.com/DAVID-NGUYEN-S16/SemViQA)  
-
-## About
-
-*Built by Dien X. Tran*
-[![LinkedIn](https://img.shields.io/badge/LinkedIn-Profile-blue?logo=linkedin)](https://www.linkedin.com/in/xndien2004/)
-For more details, visit the project repository.
-[![GitHub stars](https://img.shields.io/github/stars/DAVID-NGUYEN-S16/SemViQA?style=social)](https://github.com/DAVID-NGUYEN-S16/SemViQA)
+🔗 **Source Code:** [GitHub - SemViQA](https://github.com/DAVID-NGUYEN-S16/SemViQA)