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--- |
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base_model: hfl/chinese-macbert-base |
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datasets: |
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- CIRCL/Vulnerability-CNVD |
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library_name: transformers |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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tags: |
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- generated_from_trainer |
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- text-classification |
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- classification |
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- nlp |
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- chinese |
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- vulnerability |
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pipeline_tag: text-classification |
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language: zh |
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model-index: |
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- name: vulnerability-severity-classification-chinese-macbert-base |
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results: [] |
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--- |
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# VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification (Chinese Text) |
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This model is a fine-tuned version of [hfl/chinese-macbert-base](https://huggingface.co/hfl/chinese-macbert-base) on the dataset [CIRCL/Vulnerability-CNVD](https://huggingface.co/datasets/CIRCL/Vulnerability-CNVD). |
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For more information, visit the [Vulnerability-Lookup project page](https://vulnerability.circl.lu) or the [ML-Gateway GitHub repository](https://github.com/vulnerability-lookup/ML-Gateway), which demonstrates its usage in a FastAPI server. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6172 |
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- Accuracy: 0.7817 |
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## How to use |
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You can use this model directly with the Hugging Face `transformers` library for text classification: |
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```python |
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from transformers import pipeline |
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classifier = pipeline( |
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"text-classification", |
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model="CIRCL/vulnerability-severity-classification-chinese-macbert-base" |
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) |
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# Example usage for a Chinese vulnerability description |
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description_chinese = "TOTOLINK A3600R是中国吉翁电子（TOTOLINK）公司的一款6天线1200M无线路由器。TOTOLINK A3600R存在缓冲区溢出漏洞，该漏洞源于/cgi-bin/cstecgi.cgi文件的UploadCustomModule函数中的File参数未能正确验证输入数据的长度大小，攻击者可利用该漏洞在系统上执行任意代码或者导致拒绝服务。" |
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result_chinese = classifier(description_chinese) |
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print(result_chinese) |
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# Expected output example: [{'label': '高', 'score': 0.9802}] |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.6329 | 1.0 | 3412 | 0.5832 | 0.7546 | |
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| 0.5215 | 2.0 | 6824 | 0.5531 | 0.7750 | |
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| 0.4827 | 3.0 | 10236 | 0.5521 | 0.7768 | |
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| 0.3448 | 4.0 | 13648 | 0.5822 | 0.7814 | |
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| 0.3865 | 5.0 | 17060 | 0.6172 | 0.7817 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.7.1+cu126 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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