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# Onlyphish_100KP_BFall_fromP_10KGen_topP_0.75_noaddedB

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: 0.0211
- Accuracy: 0.9975
- F1: 0.9730
- Precision: 0.9994
- Recall: 0.948
- Roc Auc Score: 0.9740
- Tpr At Fpr 0.01: 0.9576

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.004         | 1.0   | 65938  | 0.0210          | 0.9964   | 0.9613 | 0.9966    | 0.9284 | 0.9641        | 0.9244          |
| 0.003         | 2.0   | 131876 | 0.0195          | 0.9966   | 0.9630 | 0.9970    | 0.9312 | 0.9655        | 0.9268          |
| 0.0016        | 3.0   | 197814 | 0.0148          | 0.9977   | 0.9757 | 0.9983    | 0.954  | 0.9770        | 0.9554          |
| 0.0011        | 4.0   | 263752 | 0.0202          | 0.9970   | 0.9677 | 0.9989    | 0.9384 | 0.9692        | 0.9438          |
| 0.0005        | 5.0   | 329690 | 0.0211          | 0.9975   | 0.9730 | 0.9994    | 0.948  | 0.9740        | 0.9576          |


### 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_100KP_BFall_fromP_10KGen_topP_0.75_noaddedB", "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\_100KP\_BFall\_fromP\_10KGen\_topP\_0.75\_noaddedB
============================================================


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: 0.0211
* Accuracy: 0.9975
* F1: 0.9730
* Precision: 0.9994
* Recall: 0.948
* Roc Auc Score: 0.9740
* Tpr At Fpr 0.01: 0.9576


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