bert-large-finetuned-phishing – AI Model by VijayKrish2123 | AlphaNeural AI

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VijayKrish2123/
bert-large-finetuned-phishing

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text-classification

generated_from_trainer

google-bert/bert-large-uncased

autotrain_compatible

endpoints_compatible

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bert-large-finetuned-phishing

This model is a fine-tuned version of bert-large-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1788
Accuracy: 0.9731
Precision: 0.9728
Recall: 0.9630
False Positive Rate: 0.0196

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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	False Positive Rate
0.1402	1.0	3884	0.1457	0.9585	0.9444	0.9578	0.0410
0.0808	2.0	7768	0.1541	0.9681	0.9734	0.9502	0.0189
0.0402	3.0	11652	0.1421	0.9723	0.9723	0.9615	0.0199
0.0106	4.0	15536	0.1788	0.9731	0.9728	0.9630	0.0196

Framework versions

Transformers 4.49.0
Pytorch 2.2.1+cu121
Datasets 3.3.2
Tokenizers 0.21.0