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# small-vanilla-target-glue-mnli

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: 0.6020
- Accuracy: 0.7618

## 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: 3e-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: constant
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9249        | 0.04  | 500   | 0.8197          | 0.6419   |
| 0.8154        | 0.08  | 1000  | 0.7776          | 0.6651   |
| 0.7747        | 0.12  | 1500  | 0.7455          | 0.6749   |
| 0.7523        | 0.16  | 2000  | 0.7315          | 0.6853   |
| 0.7445        | 0.2   | 2500  | 0.7097          | 0.6977   |
| 0.7337        | 0.24  | 3000  | 0.6981          | 0.7026   |
| 0.7165        | 0.29  | 3500  | 0.6876          | 0.7117   |
| 0.704         | 0.33  | 4000  | 0.6669          | 0.7192   |
| 0.68          | 0.37  | 4500  | 0.6773          | 0.7249   |
| 0.6834        | 0.41  | 5000  | 0.6513          | 0.7228   |
| 0.6808        | 0.45  | 5500  | 0.6467          | 0.7260   |
| 0.671         | 0.49  | 6000  | 0.6416          | 0.7275   |
| 0.6733        | 0.53  | 6500  | 0.6434          | 0.7372   |
| 0.6714        | 0.57  | 7000  | 0.6358          | 0.7394   |
| 0.6552        | 0.61  | 7500  | 0.6243          | 0.7423   |
| 0.6519        | 0.65  | 8000  | 0.6272          | 0.7380   |
| 0.6546        | 0.69  | 8500  | 0.6207          | 0.7440   |
| 0.6457        | 0.73  | 9000  | 0.6293          | 0.7424   |
| 0.6458        | 0.77  | 9500  | 0.6084          | 0.7492   |
| 0.6349        | 0.81  | 10000 | 0.6248          | 0.7431   |
| 0.6376        | 0.86  | 10500 | 0.6035          | 0.7531   |
| 0.6294        | 0.9   | 11000 | 0.5969          | 0.7561   |
| 0.6264        | 0.94  | 11500 | 0.5981          | 0.7518   |
| 0.6297        | 0.98  | 12000 | 0.5953          | 0.7539   |
| 0.5935        | 1.02  | 12500 | 0.6093          | 0.7602   |
| 0.5726        | 1.06  | 13000 | 0.5991          | 0.7628   |
| 0.5571        | 1.1   | 13500 | 0.5921          | 0.7606   |
| 0.5535        | 1.14  | 14000 | 0.6011          | 0.7559   |
| 0.5427        | 1.18  | 14500 | 0.6020          | 0.7618   |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "small-vanilla-target-glue-mnli", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

small-vanilla-target-glue-mnli
==============================


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: 0.6020
* Accuracy: 0.7618


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: 3e-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: constant
* num\_epochs: 200


### Training results



### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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: constant
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 3e-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: constant
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #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: 3e-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: constant
* num\_epochs: 200### Training results### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu116
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2