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bert-waf-detector
Jayant-D26/bert-waf-detector
bert-waf-detector is a text classification model from Jayant-D26. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Jan 3, 2026
- Updated
- Jan 3, 2026
- SHA
- af607180
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Base models
Model card
From the Hugging Face model README
<!-- 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. -->
bert-waf-detector
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0491
- Accuracy: 0.9916
- F1: 0.9916
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2323 | 1.0 | 330 | 0.0900 | 0.9727 | 0.9729 |
| 0.0699 | 2.0 | 660 | 0.0500 | 0.9886 | 0.9887 |
| 0.031 | 3.0 | 990 | 0.0696 | 0.9863 | 0.9865 |
| 0.0027 | 4.0 | 1320 | 0.0466 | 0.9909 | 0.9909 |
| 0.0056 | 5.0 | 1650 | 0.0491 | 0.9916 | 0.9916 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jan 3, 2026
- Updated
- Jan 3, 2026