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# K-12BERT-reward-neurallinguisticpioneers-3

This model is a fine-tuned version of [vasugoel/K-12BERT](https://huggingface.co/vasugoel/K-12BERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5952

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 11.0121       | 0.01  | 1    | 8.7103          |
| 8.6653        | 0.01  | 2    | 5.3654          |
| 4.4755        | 0.02  | 3    | 2.9993          |
| 3.5791        | 0.03  | 4    | 2.0453          |
| 2.1693        | 0.03  | 5    | 1.8209          |
| 1.8131        | 0.04  | 6    | 1.9799          |
| 2.2419        | 0.05  | 7    | 1.9990          |
| 1.3732        | 0.06  | 8    | 1.6069          |
| 1.8928        | 0.06  | 9    | 2.3442          |
| 2.0764        | 0.07  | 10   | 2.4528          |
| 1.967         | 0.08  | 11   | 1.7570          |
| 1.641         | 0.08  | 12   | 1.3257          |
| 1.7557        | 0.09  | 13   | 1.2695          |
| 0.9002        | 0.1   | 14   | 1.3756          |
| 1.2707        | 0.1   | 15   | 1.3182          |
| 0.8898        | 0.11  | 16   | 1.3691          |
| 0.7869        | 0.12  | 17   | 1.1321          |
| 1.0837        | 0.12  | 18   | 1.0468          |
| 0.6809        | 0.13  | 19   | 1.0298          |
| 0.8359        | 0.14  | 20   | 1.0701          |
| 1.532         | 0.15  | 21   | 1.1456          |
| 0.4573        | 0.15  | 22   | 1.2005          |
| 1.5712        | 0.16  | 23   | 1.5595          |
| 0.7703        | 0.17  | 24   | 1.4326          |
| 1.0659        | 0.17  | 25   | 1.0713          |
| 1.2147        | 0.18  | 26   | 0.9409          |
| 0.8567        | 0.19  | 27   | 0.9517          |
| 0.7758        | 0.19  | 28   | 1.1403          |
| 1.1653        | 0.2   | 29   | 1.2216          |
| 0.7928        | 0.21  | 30   | 1.1482          |
| 1.5701        | 0.22  | 31   | 0.9704          |
| 1.006         | 0.22  | 32   | 0.8644          |
| 0.6701        | 0.23  | 33   | 0.8915          |
| 0.8564        | 0.24  | 34   | 0.8800          |
| 0.4669        | 0.24  | 35   | 0.8653          |
| 0.6119        | 0.25  | 36   | 0.8946          |
| 0.6012        | 0.26  | 37   | 0.9236          |
| 0.3485        | 0.26  | 38   | 0.9712          |
| 1.0116        | 0.27  | 39   | 1.1866          |
| 0.5167        | 0.28  | 40   | 1.3035          |
| 0.6579        | 0.28  | 41   | 1.2122          |
| 0.4849        | 0.29  | 42   | 1.0500          |
| 0.9278        | 0.3   | 43   | 0.9969          |
| 0.6447        | 0.31  | 44   | 0.9311          |
| 0.5996        | 0.31  | 45   | 0.8287          |
| 1.2452        | 0.32  | 46   | 0.7565          |
| 1.0734        | 0.33  | 47   | 0.9004          |
| 0.8317        | 0.33  | 48   | 0.9884          |
| 1.5887        | 0.34  | 49   | 0.9487          |
| 0.8313        | 0.35  | 50   | 0.8382          |
| 0.7197        | 0.35  | 51   | 0.8620          |
| 1.2268        | 0.36  | 52   | 0.8625          |
| 0.7911        | 0.37  | 53   | 0.7812          |
| 1.0787        | 0.38  | 54   | 0.7301          |
| 0.159         | 0.38  | 55   | 0.7704          |
| 0.2064        | 0.39  | 56   | 0.7715          |
| 0.1974        | 0.4   | 57   | 0.7977          |
| 0.5762        | 0.4   | 58   | 0.9158          |
| 1.0812        | 0.41  | 59   | 0.9906          |
| 0.2053        | 0.42  | 60   | 1.0119          |
| 0.7374        | 0.42  | 61   | 0.9580          |
| 1.1598        | 0.43  | 62   | 0.9101          |
| 0.8277        | 0.44  | 63   | 0.9434          |
| 1.3316        | 0.44  | 64   | 0.9062          |
| 1.2475        | 0.45  | 65   | 0.8304          |
| 0.4621        | 0.46  | 66   | 0.7518          |
| 1.047         | 0.47  | 67   | 0.7238          |
| 1.439         | 0.47  | 68   | 0.7726          |
| 0.4821        | 0.48  | 69   | 0.9050          |
| 0.6916        | 0.49  | 70   | 0.9246          |
| 2.1817        | 0.49  | 71   | 0.8534          |
| 1.3539        | 0.5   | 72   | 0.7835          |
| 0.4156        | 0.51  | 73   | 0.6983          |
| 0.3394        | 0.51  | 74   | 0.6919          |
| 0.6992        | 0.52  | 75   | 0.6941          |
| 0.5135        | 0.53  | 76   | 0.7190          |
| 0.4799        | 0.53  | 77   | 0.7536          |
| 1.3763        | 0.54  | 78   | 0.7912          |
| 0.5585        | 0.55  | 79   | 0.7875          |
| 0.4962        | 0.56  | 80   | 0.7185          |
| 0.5592        | 0.56  | 81   | 0.6845          |
| 0.5033        | 0.57  | 82   | 0.6537          |
| 0.7098        | 0.58  | 83   | 0.6501          |
| 0.3739        | 0.58  | 84   | 0.6543          |
| 0.3426        | 0.59  | 85   | 0.6953          |
| 0.7321        | 0.6   | 86   | 0.7230          |
| 0.5637        | 0.6   | 87   | 0.7387          |
| 1.0959        | 0.61  | 88   | 0.7226          |
| 0.3723        | 0.62  | 89   | 0.7004          |
| 1.3296        | 0.62  | 90   | 0.6756          |
| 1.2918        | 0.63  | 91   | 0.6348          |
| 0.878         | 0.64  | 92   | 0.6189          |
| 0.832         | 0.65  | 93   | 0.6899          |
| 0.4342        | 0.65  | 94   | 0.8381          |
| 1.1652        | 0.66  | 95   | 0.8777          |
| 0.8581        | 0.67  | 96   | 0.7764          |
| 0.7468        | 0.67  | 97   | 0.8387          |
| 0.8492        | 0.68  | 98   | 0.7698          |
| 0.667         | 0.69  | 99   | 0.6798          |
| 1.0306        | 0.69  | 100  | 0.6889          |
| 0.4181        | 0.7   | 101  | 0.7197          |
| 0.8002        | 0.71  | 102  | 0.7047          |
| 0.4183        | 0.72  | 103  | 0.6576          |
| 0.256         | 0.72  | 104  | 0.6398          |
| 0.7134        | 0.73  | 105  | 0.6309          |
| 0.4752        | 0.74  | 106  | 0.6322          |
| 1.9373        | 0.74  | 107  | 0.7256          |
| 0.9749        | 0.75  | 108  | 0.8439          |
| 0.9102        | 0.76  | 109  | 0.8159          |
| 0.802         | 0.76  | 110  | 0.6732          |
| 0.3826        | 0.77  | 111  | 0.6126          |
| 0.2151        | 0.78  | 112  | 0.7456          |
| 0.3858        | 0.78  | 113  | 0.9544          |
| 0.8457        | 0.79  | 114  | 1.0739          |
| 1.6663        | 0.8   | 115  | 1.0784          |
| 1.6755        | 0.81  | 116  | 1.0225          |
| 27.0617       | 0.81  | 117  | 1.0269          |
| 2.0955        | 0.82  | 118  | 0.9601          |
| 0.7422        | 0.83  | 119  | 0.8699          |
| 0.5386        | 0.83  | 120  | 0.8449          |
| 0.6455        | 0.84  | 121  | 0.8467          |
| 3.3079        | 0.85  | 122  | 0.9692          |
| 0.7571        | 0.85  | 123  | 0.9999          |
| 0.9167        | 0.86  | 124  | 0.9253          |
| 0.639         | 0.87  | 125  | 0.7434          |
| 0.8654        | 0.88  | 126  | 0.6060          |
| 1.0687        | 0.88  | 127  | 0.5836          |
| 0.3017        | 0.89  | 128  | 0.5929          |
| 0.42          | 0.9   | 129  | 0.6492          |
| 0.9606        | 0.9   | 130  | 0.6870          |
| 1.2006        | 0.91  | 131  | 0.7251          |
| 0.8671        | 0.92  | 132  | 0.7631          |
| 0.6252        | 0.92  | 133  | 0.7495          |
| 1.725         | 0.93  | 134  | 0.7139          |
| 0.7052        | 0.94  | 135  | 0.6708          |
| 0.615         | 0.94  | 136  | 0.6332          |
| 0.5124        | 0.95  | 137  | 0.6807          |
| 0.6559        | 0.96  | 138  | 0.6876          |
| 0.6609        | 0.97  | 139  | 0.6254          |
| 0.3095        | 0.97  | 140  | 0.5933          |
| 0.7292        | 0.98  | 141  | 0.5738          |
| 0.8383        | 0.99  | 142  | 0.5779          |
| 0.5622        | 0.99  | 143  | 0.5934          |
| 0.307         | 1.0   | 144  | 0.5952          |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "K-12BERT-reward-neurallinguisticpioneers-3", "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 

K-12BERT-reward-neurallinguisticpioneers-3
==========================================


This model is a fine-tuned version of vasugoel/K-12BERT on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5952


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3