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# Base_100Kphish_benignFall_IL_10K_OnlyPhish_from_benign_top_p_0.75_lr1e-6

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0201
- Accuracy: 0.9978
- F1: 0.9766
- Precision: 0.9985
- Recall: 0.9556
- Roc Auc Score: 0.9778
- Tpr At Fpr 0.01: 0.9614

## 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: 1e-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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.004         | 1.0   | 6563  | 0.0144          | 0.9980   | 0.9781 | 0.9969    | 0.96   | 0.9799        | 0.9528          |
| 0.0012        | 2.0   | 13126 | 0.0202          | 0.9977   | 0.9749 | 0.9992    | 0.9518 | 0.9759        | 0.9618          |
| 0.002         | 3.0   | 19689 | 0.0176          | 0.9978   | 0.9761 | 0.9985    | 0.9546 | 0.9773        | 0.9586          |
| 0.0           | 4.0   | 26252 | 0.0205          | 0.9977   | 0.9749 | 0.9992    | 0.9518 | 0.9759        | 0.961           |
| 0.0005        | 5.0   | 32815 | 0.0201          | 0.9978   | 0.9766 | 0.9985    | 0.9556 | 0.9778        | 0.9614          |


### 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_lr1e-6", "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\_lr1e-6
===================================================================================


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


* Loss: 0.0201
* Accuracy: 0.9978
* F1: 0.9766
* Precision: 0.9985
* Recall: 0.9556
* Roc Auc Score: 0.9778
* Tpr At Fpr 0.01: 0.9614


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: 1e-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: 1e-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: 1e-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