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# add_bert_12_layer_model_complete_training_new

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.0612
- Accuracy: 0.1510

## 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: 48
- eval_batch_size: 48
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 6.6173        | 0.08  | 10000  | 6.5823          | 0.1275   |
| 6.4901        | 0.16  | 20000  | 6.3925          | 0.1398   |
| 6.2875        | 0.25  | 30000  | 6.3106          | 0.1439   |
| 6.2516        | 0.33  | 40000  | 6.2511          | 0.1465   |
| 6.2457        | 0.41  | 50000  | 6.2064          | 0.1478   |
| 6.1715        | 0.49  | 60000  | 6.1655          | 0.1486   |
| 6.0838        | 0.57  | 70000  | 6.1332          | 0.1494   |
| 6.1575        | 0.66  | 80000  | 6.1049          | 0.1503   |
| 6.0773        | 0.74  | 90000  | 6.0814          | 0.1504   |
| 6.1838        | 0.82  | 100000 | 6.0612          | 0.1510   |


### Framework versions

- Transformers 4.30.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "add_bert_12_layer_model_complete_training_new", "results": []}]}
transformers pytorch tensorboard hybridbert fill-mask generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #hybridbert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

add\_bert\_12\_layer\_model\_complete\_training\_new
====================================================


This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 6.0612
* Accuracy: 0.1510


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: 48
* eval\_batch\_size: 48
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.30.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.30.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #hybridbert #fill-mask #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: 48
* eval\_batch\_size: 48
* seed: 10
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.30.0
* Pytorch 1.14.0a0+410ce96
* Datasets 2.12.0
* Tokenizers 0.13.3