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# Base_100Kphish_benignFall_IL_10K_OnlyPhish_from_benign_top_p_0.75

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0237
- Accuracy: 0.9975
- F1: 0.9731
- Precision: 0.9983
- Recall: 0.9492
- Roc Auc Score: 0.9746
- Tpr At Fpr 0.01: 0.9508

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0042        | 1.0   | 6563  | 0.0276          | 0.9966   | 0.9628 | 0.9983    | 0.9298 | 0.9649        | 0.9308          |
| 0.0024        | 2.0   | 13126 | 0.0242          | 0.9972   | 0.9698 | 0.9973    | 0.9438 | 0.9718        | 0.927           |
| 0.0026        | 3.0   | 19689 | 0.0244          | 0.9970   | 0.9679 | 0.9987    | 0.939  | 0.9695        | 0.9514          |
| 0.0003        | 4.0   | 26252 | 0.0293          | 0.9968   | 0.9657 | 0.9989    | 0.9346 | 0.9673        | 0.9472          |
| 0.0007        | 5.0   | 32815 | 0.0237          | 0.9975   | 0.9731 | 0.9983    | 0.9492 | 0.9746        | 0.9508          |


### Framework versions

- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "Base_100Kphish_benignFall_IL_10K_OnlyPhish_from_benign_top_p_0.75", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

Base\_100Kphish\_benignFall\_IL\_10K\_OnlyPhish\_from\_benign\_top\_p\_0.75
===========================================================================


This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0237
* Accuracy: 0.9975
* F1: 0.9731
* Precision: 0.9983
* Recall: 0.9492
* Roc Auc Score: 0.9746
* Tpr At Fpr 0.01: 0.9508


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.1
* Pytorch 1.9.0+cu111
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 1.9.0+cu111
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 1.9.0+cu111
* Datasets 2.10.1
* Tokenizers 0.13.2