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# DNADebertaK6f

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: 1.3707

## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step     | Validation Loss |
|:-------------:|:------:|:--------:|:---------------:|
| 2.1545        | 1.24   | 100000   | 1.5895          |
| 1.5727        | 2.49   | 200000   | 1.5383          |
| 1.5368        | 3.73   | 300000   | 1.5117          |
| 1.5143        | 4.97   | 400000   | 1.4926          |
| 1.4969        | 6.22   | 500000   | 1.4789          |
| 1.4841        | 7.46   | 600000   | 1.4677          |
| 1.475         | 8.7    | 700000   | 1.4599          |
| 1.4677        | 9.95   | 800000   | 1.4533          |
| 1.4623        | 11.19  | 900000   | 1.4491          |
| 1.4576        | 12.43  | 1000000  | 1.4461          |
| 1.4544        | 13.68  | 1100000  | 1.4420          |
| 1.451         | 14.92  | 1200000  | 1.4381          |
| 1.4482        | 16.16  | 1300000  | 1.4359          |
| 1.446         | 17.4   | 1400000  | 1.4345          |
| 1.443         | 18.65  | 1500000  | 1.4320          |
| 1.4412        | 19.89  | 1600000  | 1.4295          |
| 1.4385        | 21.13  | 1700000  | 1.4278          |
| 1.4368        | 22.38  | 1800000  | 1.4249          |
| 1.4346        | 23.62  | 1900000  | 1.4237          |
| 1.433         | 24.86  | 2000000  | 1.4219          |
| 1.4315        | 26.11  | 2100000  | 1.4201          |
| 1.4297        | 27.35  | 2200000  | 1.4198          |
| 1.4282        | 28.59  | 2300000  | 1.4180          |
| 1.4266        | 29.84  | 2400000  | 1.4142          |
| 1.4253        | 31.08  | 2500000  | 1.4146          |
| 1.4238        | 32.32  | 2600000  | 1.4130          |
| 1.4228        | 33.57  | 2700000  | 1.4113          |
| 1.4221        | 34.81  | 2800000  | 1.4100          |
| 1.42          | 36.05  | 2900000  | 1.4097          |
| 1.4188        | 37.3   | 3000000  | 1.4085          |
| 1.4174        | 38.54  | 3100000  | 1.4067          |
| 1.4161        | 39.78  | 3200000  | 1.4064          |
| 1.4149        | 41.03  | 3300000  | 1.4058          |
| 1.4139        | 42.27  | 3400000  | 1.4024          |
| 1.4134        | 43.51  | 3500000  | 1.4022          |
| 1.4126        | 44.76  | 3600000  | 1.4025          |
| 1.4117        | 46.0   | 3700000  | 1.4015          |
| 1.411         | 47.24  | 3800000  | 1.4001          |
| 1.4098        | 48.49  | 3900000  | 1.3968          |
| 1.4096        | 49.73  | 4000000  | 1.3997          |
| 1.4089        | 50.97  | 4100000  | 1.3974          |
| 1.4084        | 52.21  | 4200000  | 1.3972          |
| 1.4072        | 53.46  | 4300000  | 1.3965          |
| 1.4066        | 54.7   | 4400000  | 1.3974          |
| 1.4062        | 55.94  | 4500000  | 1.3960          |
| 1.4058        | 57.19  | 4600000  | 1.3958          |
| 1.4053        | 58.43  | 4700000  | 1.3950          |
| 1.4041        | 59.67  | 4800000  | 1.3936          |
| 1.4041        | 60.92  | 4900000  | 1.3963          |
| 1.4031        | 62.16  | 5000000  | 1.3915          |
| 1.4023        | 63.4   | 5100000  | 1.3917          |
| 1.4022        | 64.65  | 5200000  | 1.3930          |
| 1.4017        | 65.89  | 5300000  | 1.3904          |
| 1.4009        | 67.13  | 5400000  | 1.3899          |
| 1.4007        | 68.38  | 5500000  | 1.3892          |
| 1.3997        | 69.62  | 5600000  | 1.3910          |
| 1.3996        | 70.86  | 5700000  | 1.3892          |
| 1.3991        | 72.11  | 5800000  | 1.3890          |
| 1.3983        | 73.35  | 5900000  | 1.3870          |
| 1.3985        | 74.59  | 6000000  | 1.3889          |
| 1.3975        | 75.84  | 6100000  | 1.3865          |
| 1.3973        | 77.08  | 6200000  | 1.3852          |
| 1.3969        | 78.32  | 6300000  | 1.3869          |
| 1.3964        | 79.57  | 6400000  | 1.3843          |
| 1.396         | 80.81  | 6500000  | 1.3853          |
| 1.3955        | 82.05  | 6600000  | 1.3844          |
| 1.3952        | 83.3   | 6700000  | 1.3863          |
| 1.395         | 84.54  | 6800000  | 1.3835          |
| 1.3948        | 85.78  | 6900000  | 1.3841          |
| 1.394         | 87.02  | 7000000  | 1.3850          |
| 1.3934        | 88.27  | 7100000  | 1.3827          |
| 1.3932        | 89.51  | 7200000  | 1.3830          |
| 1.3929        | 90.75  | 7300000  | 1.3821          |
| 1.392         | 92.0   | 7400000  | 1.3820          |
| 1.392         | 93.24  | 7500000  | 1.3837          |
| 1.3913        | 94.48  | 7600000  | 1.3817          |
| 1.3909        | 95.73  | 7700000  | 1.3836          |
| 1.3906        | 96.97  | 7800000  | 1.3811          |
| 1.3903        | 98.21  | 7900000  | 1.3806          |
| 1.3902        | 99.46  | 8000000  | 1.3807          |
| 1.3896        | 100.7  | 8100000  | 1.3804          |
| 1.3895        | 101.94 | 8200000  | 1.3805          |
| 1.3891        | 103.19 | 8300000  | 1.3821          |
| 1.3889        | 104.43 | 8400000  | 1.3833          |
| 1.3881        | 105.67 | 8500000  | 1.3788          |
| 1.388         | 106.92 | 8600000  | 1.3818          |
| 1.3876        | 108.16 | 8700000  | 1.3806          |
| 1.387         | 109.4  | 8800000  | 1.3766          |
| 1.387         | 110.65 | 8900000  | 1.3765          |
| 1.3865        | 111.89 | 9000000  | 1.3800          |
| 1.3864        | 113.13 | 9100000  | 1.3830          |
| 1.386         | 114.38 | 9200000  | 1.3770          |
| 1.3853        | 115.62 | 9300000  | 1.3771          |
| 1.3852        | 116.86 | 9400000  | 1.3772          |
| 1.3848        | 118.1  | 9500000  | 1.3771          |
| 1.384         | 119.35 | 9600000  | 1.3749          |
| 1.3843        | 120.59 | 9700000  | 1.3764          |
| 1.3836        | 121.83 | 9800000  | 1.3802          |
| 1.3833        | 123.08 | 9900000  | 1.3756          |
| 1.3831        | 124.32 | 10000000 | 1.3748          |
| 1.3821        | 125.56 | 10100000 | 1.3755          |
| 1.3817        | 126.81 | 10200000 | 1.3744          |
| 1.3819        | 128.05 | 10300000 | 1.3763          |
| 1.381         | 129.29 | 10400000 | 1.3743          |
| 1.3805        | 130.54 | 10500000 | 1.3762          |
| 1.3804        | 131.78 | 10600000 | 1.3725          |
| 1.3795        | 133.02 | 10700000 | 1.3753          |
| 1.3791        | 134.27 | 10800000 | 1.3780          |
| 1.3785        | 135.51 | 10900000 | 1.3749          |
| 1.3781        | 136.75 | 11000000 | 1.3749          |
| 1.3779        | 138.0  | 11100000 | 1.3737          |
| 1.3772        | 139.24 | 11200000 | 1.3715          |
| 1.3763        | 140.48 | 11300000 | 1.3759          |
| 1.3761        | 141.73 | 11400000 | 1.3745          |
| 1.3752        | 142.97 | 11500000 | 1.3708          |
| 1.3744        | 144.21 | 11600000 | 1.3735          |
| 1.3736        | 145.46 | 11700000 | 1.3720          |
| 1.3728        | 146.7  | 11800000 | 1.3702          |
| 1.3714        | 147.94 | 11900000 | 1.3696          |
| 1.3706        | 149.19 | 12000000 | 1.3707          |


### Framework versions

- Transformers 4.27.3
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "DNADebertaK6f", "results": []}]}
transformers pytorch deberta fill-mask generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

DNADebertaK6f
=============


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


* Loss: 1.3707


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: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 150
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 2.0.0
* Datasets 2.10.1
* 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: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 150
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #deberta #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: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 150
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #deberta #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: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 150
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.3
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2