|
|
--- |
|
|
library_name: transformers |
|
|
license: apache-2.0 |
|
|
base_model: hfl/chinese-macbert-base |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
model-index: |
|
|
- name: vulnerability-severity-classification-chinese-macbert-base |
|
|
results: [] |
|
|
datasets: |
|
|
- CIRCL/Vulnerability-CNVD |
|
|
--- |
|
|
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
|
|
# vulnerability-severity-classification-chinese-macbert-base |
|
|
|
|
|
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). |
|
|
|
|
|
You can read [this page](https://www.vulnerability-lookup.org/user-manual/ai/) for more information. |
|
|
|
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.5994 |
|
|
- Accuracy: 0.7900 |
|
|
|
|
|
## Training procedure |
|
|
|
|
|
### Training hyperparameters |
|
|
|
|
|
The following hyperparameters were used during training: |
|
|
- learning_rate: 3e-05 |
|
|
- train_batch_size: 32 |
|
|
- eval_batch_size: 32 |
|
|
- seed: 42 |
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 5 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
|
|
| 0.65 | 1.0 | 3388 | 0.5772 | 0.7561 | |
|
|
| 0.582 | 2.0 | 6776 | 0.5656 | 0.7620 | |
|
|
| 0.5284 | 3.0 | 10164 | 0.5274 | 0.7881 | |
|
|
| 0.3406 | 4.0 | 13552 | 0.5555 | 0.7869 | |
|
|
| 0.3224 | 5.0 | 16940 | 0.5994 | 0.7900 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.51.3 |
|
|
- Pytorch 2.7.1+cu126 |
|
|
- Datasets 3.6.0 |
|
|
- Tokenizers 0.21.1 |
