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# MixGPT2_100KP_BFall_fromB_20KGen_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: 0.0189
- Accuracy: 0.9972
- F1: 0.9700
- Precision: 0.9994
- Recall: 0.9424
- Roc Auc Score: 0.9712
- Tpr At Fpr 0.01: 0.9544

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0051        | 1.0   | 78750  | 0.0225          | 0.9964   | 0.9605 | 0.9961    | 0.9274 | 0.9636        | 0.9226          |
| 0.0044        | 2.0   | 157500 | 0.0219          | 0.9963   | 0.9593 | 0.9985    | 0.923  | 0.9615        | 0.933           |
| 0.0018        | 3.0   | 236250 | 0.0216          | 0.9969   | 0.9669 | 0.9991    | 0.9366 | 0.9683        | 0.9496          |
| 0.0012        | 4.0   | 315000 | 0.0233          | 0.9967   | 0.9646 | 0.9994    | 0.9322 | 0.9661        | 0.9448          |
| 0.0011        | 5.0   | 393750 | 0.0189          | 0.9972   | 0.9700 | 0.9994    | 0.9424 | 0.9712        | 0.9544          |


### 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": "MixGPT2_100KP_BFall_fromB_20KGen_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 

MixGPT2\_100KP\_BFall\_fromB\_20KGen\_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: 0.0189
* Accuracy: 0.9972
* F1: 0.9700
* Precision: 0.9994
* Recall: 0.9424
* Roc Auc Score: 0.9712
* Tpr At Fpr 0.01: 0.9544


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