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# bert-trainer-8b

This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1639

## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 32
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.5416        | 1.0   | 500   | 6.5207          |
| 6.393         | 1.99  | 1000  | 6.3903          |
| 6.2817        | 2.99  | 1500  | 6.3033          |
| 6.2274        | 3.98  | 2000  | 6.2671          |
| 6.179         | 4.98  | 2500  | 6.2431          |
| 6.1684        | 5.98  | 3000  | 6.2309          |
| 6.1244        | 6.97  | 3500  | 6.2114          |
| 6.0879        | 7.97  | 4000  | 6.1932          |
| 6.0643        | 8.96  | 4500  | 6.1791          |
| 6.0481        | 9.96  | 5000  | 6.1638          |
| 6.0231        | 10.96 | 5500  | 6.1581          |
| 5.9987        | 11.95 | 6000  | 6.1365          |
| 5.9989        | 12.95 | 6500  | 6.1194          |
| 5.9535        | 13.94 | 7000  | 6.1095          |
| 5.9139        | 14.94 | 7500  | 6.0890          |
| 5.8462        | 15.94 | 8000  | 6.0224          |
| 5.7689        | 16.93 | 8500  | 5.9266          |
| 5.6137        | 17.93 | 9000  | 5.7195          |
| 4.7163        | 18.92 | 9500  | 4.6131          |
| 4.0877        | 19.92 | 10000 | 4.0903          |
| 3.7832        | 20.92 | 10500 | 3.8340          |
| 3.6104        | 21.91 | 11000 | 3.6572          |
| 3.4615        | 22.91 | 11500 | 3.5278          |
| 3.3661        | 23.9  | 12000 | 3.4201          |
| 3.271         | 24.9  | 12500 | 3.3333          |
| 3.2179        | 25.9  | 13000 | 3.2720          |
| 3.1759        | 26.89 | 13500 | 3.2317          |
| 3.1419        | 27.89 | 14000 | 3.2006          |
| 3.1041        | 28.88 | 14500 | 3.1806          |
| 3.0836        | 29.88 | 15000 | 3.1693          |
| 3.0998        | 30.88 | 15500 | 3.1679          |
| 3.08          | 31.87 | 16000 | 3.1639          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["generator"], "model-index": [{"name": "bert-trainer-8b", "results": []}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer dataset:generator autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-generator #autotrain_compatible #endpoints_compatible #region-us 

bert-trainer-8b
===============


This model is a fine-tuned version of [](URL on the generator dataset.
It achieves the following results on the evaluation set:


* Loss: 3.1639


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: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 32
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 32
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-generator #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 32
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2