
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert_12_layer_model_v1

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: 2.1713
- Accuracy: 0.5905

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 6.293         | 1.0   | 22886  | 5.6395          | 0.1723   |
| 3.7477        | 2.0   | 45772  | 2.6625          | 0.5252   |
| 2.5641        | 3.0   | 68658  | 2.3431          | 0.5673   |
| 2.3563        | 4.0   | 91544  | 2.2188          | 0.5839   |
| 2.2692        | 5.0   | 114430 | 2.1713          | 0.5905   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert_12_layer_model_v1", "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 

bert\_12\_layer\_model\_v1
==========================


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


* Loss: 2.1713
* Accuracy: 0.5905


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: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 10
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.14.0a0+410ce96
* Datasets 2.10.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 10
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.14.0a0+410ce96
* Datasets 2.10.0
* Tokenizers 0.13.2
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: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 10
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.14.0a0+410ce96
* Datasets 2.10.0
* Tokenizers 0.13.2
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: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 10
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10000
* num\_epochs: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.14.0a0+410ce96
* Datasets 2.10.0
* Tokenizers 0.13.2