bert-waf-detector – AI Model by Jayant-D26 | AlphaNeural AI

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Jayant-D26/
bert-waf-detector

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

text-embeddings-inference

endpoints_compatible

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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