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# results

This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6627

## 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: 8
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 0.49  | 500   | 3.5536          |
| 3.752         | 0.97  | 1000  | 3.0406          |
| 3.752         | 1.46  | 1500  | 2.7601          |
| 2.6844        | 1.94  | 2000  | 2.5655          |
| 2.6844        | 2.43  | 2500  | 2.4174          |
| 2.3487        | 2.91  | 3000  | 2.3163          |
| 2.3487        | 3.4   | 3500  | 2.2146          |
| 2.1554        | 3.89  | 4000  | 2.1560          |
| 2.1554        | 4.37  | 4500  | 2.0935          |
| 2.019         | 4.86  | 5000  | 2.0375          |
| 2.019         | 5.34  | 5500  | 1.9942          |
| 1.9254        | 5.83  | 6000  | 1.9530          |
| 1.9254        | 6.32  | 6500  | 1.9215          |
| 1.8506        | 6.8   | 7000  | 1.8908          |
| 1.8506        | 7.29  | 7500  | 1.8693          |
| 1.793         | 7.77  | 8000  | 1.8399          |
| 1.793         | 8.26  | 8500  | 1.8191          |
| 1.7425        | 8.75  | 9000  | 1.8016          |
| 1.7425        | 9.23  | 9500  | 1.7760          |
| 1.7093        | 9.72  | 10000 | 1.7668          |
| 1.7093        | 10.2  | 10500 | 1.7474          |
| 1.6754        | 10.69 | 11000 | 1.7365          |
| 1.6754        | 11.18 | 11500 | 1.7229          |
| 1.6501        | 11.66 | 12000 | 1.7145          |
| 1.6501        | 12.15 | 12500 | 1.7029          |
| 1.633         | 12.63 | 13000 | 1.6965          |
| 1.633         | 13.12 | 13500 | 1.6878          |
| 1.6153        | 13.61 | 14000 | 1.6810          |
| 1.6153        | 14.09 | 14500 | 1.6775          |
| 1.6043        | 14.58 | 15000 | 1.6720          |
| 1.6043        | 15.06 | 15500 | 1.6719          |
| 1.5942        | 15.55 | 16000 | 1.6602          |
| 1.5942        | 16.03 | 16500 | 1.6643          |
| 1.5869        | 16.52 | 17000 | 1.6632          |
| 1.5869        | 17.01 | 17500 | 1.6551          |
| 1.5834        | 17.49 | 18000 | 1.6557          |
| 1.5834        | 17.98 | 18500 | 1.6561          |
| 1.5755        | 18.46 | 19000 | 1.6620          |
| 1.5755        | 18.95 | 19500 | 1.6524          |
| 1.5823        | 19.44 | 20000 | 1.6536          |
| 1.5823        | 19.92 | 20500 | 1.6627          |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "results", "results": []}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

results
=======


This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.6627


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: 8
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1