metadata

library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: phishing-binary-classification-bert
    results: []
datasets:
  - aisuko/phishing-binary-classification
language:
  - en

phishing-binary-classification-bert

This model is a fine-tuned version of google-bert/bert-base-uncased on aisuko/phishing-binary-classification dataset. It achieves the following results on the evaluation set:

Loss: 0.4878
Accuracy: 0.82
Auc: 0.919

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

aisuko/phishing-binary-classification dataset

Training procedure

Please check Kaggle notbebook FT Google Bert for Binary Classification

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Auc
0.6681	1.0	1250	0.6198	0.69	0.885
0.6185	2.0	2500	0.5813	0.712	0.897
0.5907	3.0	3750	0.5478	0.82	0.9
0.5693	4.0	5000	0.5267	0.815	0.908
0.5608	5.0	6250	0.5193	0.787	0.91
0.5486	6.0	7500	0.5168	0.769	0.915
0.5409	7.0	8750	0.5034	0.79	0.916
0.5338	8.0	10000	0.5016	0.784	0.918
0.5331	9.0	11250	0.4947	0.796	0.919
0.5308	10.0	12500	0.4878	0.82	0.919

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0