metadata
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: RoBERTa-WebAttack
results: []
RoBERTa-WebAttack
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0133
- F1: 0.9974
- Accuracy: 0.9974
- Precision: 0.9974
- Recall: 0.9974
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: 5e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0207 | 1.0 | 3713 | 0.0229 | 0.9956 | 0.9956 | 0.9956 | 0.9956 |
| 0.0215 | 2.0 | 7426 | 0.0158 | 0.9963 | 0.9963 | 0.9963 | 0.9963 |
| 0.001 | 3.0 | 11139 | 0.0133 | 0.9974 | 0.9974 | 0.9974 | 0.9974 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1