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---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
- cybersecurity
metrics:
- accuracy
model-index:
- name: my_awesome_model
results: []
pipeline_tag: text-classification
---
<!-- 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. -->
# vuln-cat
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5132
- Accuracy: 0.9034
## Model description
vuln-cat is a classification model based on fine-tuning of scibert. It categorizes CVE summaries into 11 types of vulnerabilities, with class labels including:
```
[
'csrf',
'directory_traversal',
'file_inclusion',
'input_validation',
'memory_corruption',
'open_redirect',
'overflow',
'sql_injection',
'ssrf',
'xss',
'xxe'
]
```
## Usage
```python
from transformers import pipeline
text = 'A path traversal exists in a specific dll of Trend Micro Mobile Security (Enterprise) 9.8 SP5 which could allow an authenticated remote attacker to delete arbitrary files.'
classifier = pipeline(
"text-classification",
model="conflick0/vuln-cat",
padding=True,
truncation=True,
max_length=512,
)
classifier(text)
# [{'label': 'directory_traversal', 'score': 0.9969494938850403}]
```
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 88 | 0.3975 | 0.9006 |
| No log | 2.0 | 176 | 0.3922 | 0.9034 |
| No log | 3.0 | 264 | 0.4732 | 0.9034 |
| No log | 4.0 | 352 | 0.5226 | 0.8949 |
| No log | 5.0 | 440 | 0.4903 | 0.9034 |
| 0.0513 | 6.0 | 528 | 0.5203 | 0.9062 |
| 0.0513 | 7.0 | 616 | 0.5192 | 0.8949 |
| 0.0513 | 8.0 | 704 | 0.5132 | 0.9034 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |