File size: 10,005 Bytes
06c1299 d5e135c 06c1299 dbda129 06c1299 66c1fbc 39aaf27 66c1fbc 06c1299 66c1fbc 06c1299 66c1fbc 06c1299 dbda129 06c1299 1071f4b 06c1299 1071f4b 06c1299 |
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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 |
---
language:
- vi
library_name: transformers
pipeline_tag: text-classification
license: mit
tags:
- SemViQA
- binary-classification
- fact-checking
---
# 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](https://huggingface.co/SemViQA)
- **Fine-tuned model:** [InfoXLM](https://huggingface.co/microsoft/infoxlm-large)
- **Supported Language:** Vietnamese
- **Task:** Binary Classification (Fact Verification)
- **Dataset:** [ViWikiFC](https://arxiv.org/abs/2405.07615)
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
```Python
# 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-infoxlm-viwikifc")
model = ClaimModelForClassification.from_pretrained("SemViQA/bc-infoxlm-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 0.0001
# 2) REFUTED 0.9999
```
## **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.
<table>
<thead>
<tr>
<th colspan="2">Method</th>
<th colspan="4">ViWikiFC</th>
</tr>
<tr>
<th>ER</th>
<th>VC</th>
<th>Strict Acc</th>
<th>VC Acc</th>
<th>ER Acc</th>
<th>Time (s)</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">TF-IDF</td>
<td>InfoXLM<sub>large</sub></td>
<td>75.56</td>
<td>82.21</td>
<td>90.15</td>
<td>131</td>
</tr>
<tr>
<td>XLM-R<sub>large</sub></td>
<td>76.47</td>
<td>82.78</td>
<td>90.15</td>
<td>134</td>
</tr>
<tr>
<td>Ernie-M<sub>large</sub></td>
<td>75.56</td>
<td>81.83</td>
<td>90.15</td>
<td>144</td>
</tr>
<tr>
<td rowspan="3">BM25</td>
<td>InfoXLM<sub>large</sub></td>
<td>70.44</td>
<td>79.01</td>
<td>83.50</td>
<td>130</td>
</tr>
<tr>
<td>XLM-R<sub>large</sub></td>
<td>70.97</td>
<td>78.91</td>
<td>83.50</td>
<td>132</td>
</tr>
<tr>
<td>Ernie-M<sub>large</sub></td>
<td>70.21</td>
<td>78.29</td>
<td>83.50</td>
<td>141</td>
</tr>
<tr>
<td rowspan="3">SBert</td>
<td>InfoXLM<sub>large</sub></td>
<td>74.99</td>
<td>81.59</td>
<td>89.72</td>
<td>195</td>
</tr>
<tr>
<td>XLM-R<sub>large</sub></td>
<td>75.80</td>
<td>82.35</td>
<td>89.72</td>
<td>194</td>
</tr>
<tr>
<td>Ernie-M<sub>large</sub></td>
<td>75.13</td>
<td>81.44</td>
<td>89.72</td>
<td>203</td>
</tr>
<tr>
<th colspan="1">QA-based approaches</th>
<th colspan="1">VC</th>
<th colspan="4"></th>
</tr>
<tr>
<td rowspan="3">ViMRC<sub>large</sub></td>
<td>InfoXLM<sub>large</sub></td>
<td>77.28</td>
<td>81.97</td>
<td>92.49</td>
<td>3778</td>
</tr>
<tr>
<td>XLM-R<sub>large</sub></td>
<td>78.29</td>
<td>82.83</td>
<td>92.49</td>
<td>3824</td>
</tr>
<tr>
<td>Ernie-M<sub>large</sub></td>
<td>77.38</td>
<td>81.92</td>
<td>92.49</td>
<td>3785</td>
</tr>
<tr>
<td rowspan="3">InfoXLM<sub>large</sub></td>
<td>InfoXLM<sub>large</sub></td>
<td>78.14</td>
<td>82.07</td>
<td>93.45</td>
<td>4092</td>
</tr>
<tr>
<td>XLM-R<sub>large</sub></td>
<td>79.20</td>
<td>83.07</td>
<td>93.45</td>
<td>4096</td>
</tr>
<tr>
<td>Ernie-M<sub>large</sub></td>
<td>78.24</td>
<td>82.21</td>
<td>93.45</td>
<td>4102</td>
</tr>
<tr>
<th colspan="2">LLM</th>
<th colspan="4"></th>
</tr>
<tr>
<td colspan="2">Qwen2.5-1.5B-Instruct</td>
<td>51.03</td>
<td>65.18</td>
<td>78.96</td>
<td>7665</td>
</tr>
<tr>
<td colspan="2">Qwen2.5-3B-Instruct</td>
<td>44.38</td>
<td>62.31</td>
<td>71.35</td>
<td>12123</td>
</tr>
<tr>
<th colspan="1">LLM</th>
<th colspan="1">VC</th>
<th colspan="4"></th>
</tr>
<tr>
<td rowspan="3">Qwen2.5-1.5B-Instruct</td>
<td>InfoXLM<sub>large</sub></td>
<td>66.14</td>
<td>76.47</td>
<td>78.96</td>
<td>7788</td>
</tr>
<tr>
<td>XLM-R<sub>large</sub></td>
<td>67.67</td>
<td>78.10</td>
<td>78.96</td>
<td>7789</td>
</tr>
<tr>
<td>Ernie-M<sub>large</sub></td>
<td>66.52</td>
<td>76.52</td>
<td>78.96</td>
<td>7794</td>
</tr>
<tr>
<td rowspan="3">Qwen2.5-3B-Instruct</td>
<td>InfoXLM<sub>large</sub></td>
<td>59.88</td>
<td>72.50</td>
<td>71.35</td>
<td>12246</td>
</tr>
<tr>
<td>XLM-R<sub>large</sub></td>
<td>60.74</td>
<td>73.08</td>
<td>71.35</td>
<td>12246</td>
</tr>
<tr>
<td>Ernie-M<sub>large</sub></td>
<td>60.02</td>
<td>72.21</td>
<td>71.35</td>
<td>12251</td>
</tr>
<tr>
<th colspan="1">SER Faster (ours)</th>
<th colspan="1">TVC (ours)</th>
<th colspan="4"></th>
</tr>
<tr>
<td>TF-IDF + ViMRC<sub>large</sub></td>
<td>Ernie-M<sub>large</sub></td>
<td style="color:blue">79.44</td>
<td style="color:blue">82.93</td>
<td style="color:blue">94.60</td>
<td style="color:blue">410</td>
</tr>
<tr>
<td>TF-IDF + InfoXLM<sub>large</sub></td>
<td>Ernie-M<sub>large</sub></td>
<td style="color:blue">79.77</td>
<td style="color:blue">83.07</td>
<td style="color:blue">95.03</td>
<td style="color:blue">487</td>
</tr>
<tr>
<th colspan="1">SER (ours)</th>
<th colspan="1">TVC (ours)</th>
<th colspan="4"></th>
</tr>
<tr>
<td rowspan="3">TF-IDF + ViMRC<sub>large</sub></td>
<td>InfoXLM<sub>large</sub></td>
<td>80.25</td>
<td>83.84</td>
<td>94.69</td>
<td>2731</td>
</tr>
<tr>
<td>XLM-R<sub>large</sub></td>
<td>80.34</td>
<td>83.64</td>
<td>94.69</td>
<td>2733</td>
</tr>
<tr>
<td>Ernie-M<sub>large</sub></td>
<td>79.53</td>
<td>82.97</td>
<td>94.69</td>
<td>2733</td>
</tr>
<tr>
<td rowspan="3">TF-IDF + InfoXLM<sub>large</sub></td>
<td>InfoXLM<sub>large</sub></td>
<td>80.68</td>
<td><strong>83.98</strong></td>
<td><strong>95.31</strong></td>
<td>3860</td>
</tr>
<tr>
<td>XLM-R<sub>large</sub></td>
<td><strong>80.82</strong></td>
<td>83.88</td>
<td><strong>95.31</strong></td>
<td>3843</td>
</tr>
<tr>
<td>Ernie-M<sub>large</sub></td>
<td>80.06</td>
<td>83.17</td>
<td><strong>95.31</strong></td>
<td>3891</td>
</tr>
</tbody>
</table>
## **Citation**
If you use **SemViQA-BC** in your research, please cite:
```bibtex
@misc{tran2025semviqasemanticquestionanswering,
title={SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking},
author={Dien X. Tran and Nam V. Nguyen 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](https://arxiv.org/abs/2503.00955)
🔗 **Source Code:** [GitHub - SemViQA](https://github.com/DAVID-NGUYEN-S16/SemViQA)
## About
*Built by Dien X. Tran*
[](https://www.linkedin.com/in/xndien2004/)
For more details, visit the project repository.
[](https://github.com/DAVID-NGUYEN-S16/SemViQA) |