File size: 1,977 Bytes
37d2357 9e4a6b1 37d2357 7cbb402 37d2357 7cbb402 37d2357 7cbb402 37d2357 25b8d23 37d2357 |
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 |
---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vulnerability-severity-classification-roberta-base-expC
results: []
---
<!-- 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-roberta-base-expC
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5068
- Accuracy: 0.8484
## Model description
More information needed
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.661 | 1.0 | 14844 | 0.6385 | 0.7360 |
| 0.5927 | 2.0 | 29688 | 0.5854 | 0.7616 |
| 0.5032 | 3.0 | 44532 | 0.5394 | 0.7890 |
| 0.5241 | 4.0 | 59376 | 0.5038 | 0.8080 |
| 0.3666 | 5.0 | 74220 | 0.4894 | 0.8245 |
| 0.3042 | 6.0 | 89064 | 0.4862 | 0.8347 |
| 0.3709 | 7.0 | 103908 | 0.4872 | 0.8447 |
| 0.1723 | 8.0 | 118752 | 0.5068 | 0.8484 |
### Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
|