metadata
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
vulnerability-severity-classification-chinese-macbert-base
This model is a fine-tuned version of hfl/chinese-macbert-base on the dataset CIRCL/Vulnerability-CNVD.
You can read this page for more information.
It achieves the following results on the evaluation set:
- Loss: 0.6177
- Accuracy: 0.7801
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 |
|---|---|---|---|---|
| 0.6391 | 1.0 | 3388 | 0.5728 | 0.7547 |
| 0.6062 | 2.0 | 6776 | 0.5545 | 0.7704 |
| 0.5545 | 3.0 | 10164 | 0.5357 | 0.7819 |
| 0.3597 | 4.0 | 13552 | 0.5709 | 0.7820 |
| 0.327 | 5.0 | 16940 | 0.6177 | 0.7801 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1