phishing_3_1 – AI Model by hoanganhvu | AlphaNeural AI

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hoanganhvu/
phishing_3_1

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google-bert/bert-large-uncased

endpoints_compatible

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phishing_3_1

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: 0.5678
Accuracy: 0.9837
Precision: 0.9884
Recall: 0.9788
False Positive Rate: 0.0115

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: 12
eval_batch_size: 12
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	False Positive Rate
0.5925	1.0	3025	0.5767	0.9743	0.9853	0.9630	0.0143
0.5784	2.0	6050	0.5709	0.9802	0.9764	0.9841	0.0238
0.5766	3.0	9075	0.6025	0.9490	0.9968	0.9008	0.0029
0.5682	4.0	12100	0.5678	0.9837	0.9884	0.9788	0.0115

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2