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# Onlyphish_10K_fromP_BFall_10KGen_topP_0.75

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0759
- Accuracy: 0.9929
- F1: 0.9193
- Precision: 1.0
- Recall: 0.8506
- Roc Auc Score: 0.9253
- Tpr At Fpr 0.01: 0.8776

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0055        | 1.0   | 13125 | 0.0436          | 0.9901   | 0.8844 | 0.9933    | 0.797  | 0.8984        | 0.7488          |
| 0.0032        | 2.0   | 26250 | 0.1145          | 0.9853   | 0.8171 | 0.9994    | 0.691  | 0.8455        | 0.756           |
| 0.0025        | 3.0   | 39375 | 0.0705          | 0.9919   | 0.9076 | 0.9978    | 0.8324 | 0.9162        | 0.8332          |
| 0.0018        | 4.0   | 52500 | 0.0848          | 0.9919   | 0.9065 | 0.9998    | 0.8292 | 0.9146        | 0.8506          |
| 0.0008        | 5.0   | 65625 | 0.0759          | 0.9929   | 0.9193 | 1.0       | 0.8506 | 0.9253        | 0.8776          |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "Onlyphish_10K_fromP_BFall_10KGen_topP_0.75", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Onlyphish\_10K\_fromP\_BFall\_10KGen\_topP\_0.75
================================================


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


* Loss: 0.0759
* Accuracy: 0.9929
* F1: 0.9193
* Precision: 1.0
* Recall: 0.8506
* Roc Auc Score: 0.9253
* Tpr At Fpr 0.01: 0.8776


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 #license-apache-2.0 #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