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# KannadaBERT-lamb

This model is a fine-tuned version of [Chakita/KannadaBERT](https://huggingface.co/Chakita/KannadaBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2098

## 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: 2e-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: linear
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 1.0   | 319   | 3.6259          |
| 3.9523        | 2.0   | 638   | 3.6620          |
| 3.9523        | 3.0   | 957   | 3.5293          |
| 3.7334        | 4.0   | 1276  | 3.6279          |
| 3.6238        | 5.0   | 1595  | 3.4369          |
| 3.6238        | 6.0   | 1914  | 3.4624          |
| 3.5668        | 7.0   | 2233  | 3.3384          |
| 3.5026        | 8.0   | 2552  | 3.4141          |
| 3.5026        | 9.0   | 2871  | 3.3134          |
| 3.4171        | 10.0  | 3190  | 3.2900          |
| 3.358         | 11.0  | 3509  | 3.1716          |
| 3.358         | 12.0  | 3828  | 3.1200          |
| 3.2705        | 13.0  | 4147  | 3.1204          |
| 3.2705        | 14.0  | 4466  | 3.0478          |
| 3.1964        | 15.0  | 4785  | 2.9918          |
| 3.1388        | 16.0  | 5104  | 3.0790          |
| 3.1388        | 17.0  | 5423  | 2.9753          |
| 3.094         | 18.0  | 5742  | 2.9102          |
| 3.0608        | 19.0  | 6061  | 2.8584          |
| 3.0608        | 20.0  | 6380  | 2.7935          |
| 3.0029        | 21.0  | 6699  | 2.7997          |
| 2.9807        | 22.0  | 7018  | 2.8857          |
| 2.9807        | 23.0  | 7337  | 2.8606          |
| 2.9287        | 24.0  | 7656  | 2.8186          |
| 2.9287        | 25.0  | 7975  | 2.7788          |
| 2.894         | 26.0  | 8294  | 2.7850          |
| 2.8592        | 27.0  | 8613  | 2.6883          |
| 2.8592        | 28.0  | 8932  | 2.7090          |
| 2.8158        | 29.0  | 9251  | 2.5496          |
| 2.7705        | 30.0  | 9570  | 2.7173          |
| 2.7705        | 31.0  | 9889  | 2.5792          |
| 2.7425        | 32.0  | 10208 | 2.5922          |
| 2.7014        | 33.0  | 10527 | 2.5099          |
| 2.7014        | 34.0  | 10846 | 2.5285          |
| 2.6643        | 35.0  | 11165 | 2.5281          |
| 2.6643        | 36.0  | 11484 | 2.4245          |
| 2.6002        | 37.0  | 11803 | 2.4300          |
| 2.5816        | 38.0  | 12122 | 2.3803          |
| 2.5816        | 39.0  | 12441 | 2.2476          |
| 2.5257        | 40.0  | 12760 | 2.2816          |
| 2.5016        | 41.0  | 13079 | 2.2871          |
| 2.5016        | 42.0  | 13398 | 2.2967          |
| 2.4552        | 43.0  | 13717 | 2.2689          |
| 2.4262        | 44.0  | 14036 | 2.2345          |
| 2.4262        | 45.0  | 14355 | 2.3149          |
| 2.3905        | 46.0  | 14674 | 2.2341          |
| 2.3905        | 47.0  | 14993 | 2.1906          |
| 2.3551        | 48.0  | 15312 | 2.2131          |
| 2.3406        | 49.0  | 15631 | 2.1505          |
| 2.3406        | 50.0  | 15950 | 2.1251          |
| 2.3076        | 51.0  | 16269 | 2.1266          |
| 2.2673        | 52.0  | 16588 | 2.1688          |
| 2.2673        | 53.0  | 16907 | 2.0224          |
| 2.249         | 54.0  | 17226 | 2.0511          |
| 2.2314        | 55.0  | 17545 | 2.0409          |
| 2.2314        | 56.0  | 17864 | 2.0156          |
| 2.1968        | 57.0  | 18183 | 2.0767          |
| 2.1638        | 58.0  | 18502 | 1.9530          |
| 2.1638        | 59.0  | 18821 | 2.0355          |
| 2.1452        | 60.0  | 19140 | 1.9454          |
| 2.1452        | 61.0  | 19459 | 1.9321          |
| 2.1209        | 62.0  | 19778 | 1.9333          |
| 2.0946        | 63.0  | 20097 | 1.8804          |
| 2.0946        | 64.0  | 20416 | 1.9018          |
| 2.074         | 65.0  | 20735 | 1.8112          |
| 2.0394        | 66.0  | 21054 | 1.8049          |
| 2.0394        | 67.0  | 21373 | 1.8753          |
| 2.0188        | 68.0  | 21692 | 1.7983          |
| 1.99          | 69.0  | 22011 | 1.8464          |
| 1.99          | 70.0  | 22330 | 1.7434          |
| 1.9644        | 71.0  | 22649 | 1.7874          |
| 1.9644        | 72.0  | 22968 | 1.7875          |
| 1.9533        | 73.0  | 23287 | 1.8250          |
| 1.926         | 74.0  | 23606 | 1.7371          |
| 1.926         | 75.0  | 23925 | 1.7980          |
| 1.9171        | 76.0  | 24244 | 1.7354          |
| 1.8955        | 77.0  | 24563 | 1.8076          |
| 1.8955        | 78.0  | 24882 | 1.6711          |
| 1.878         | 79.0  | 25201 | 1.6909          |
| 1.8386        | 80.0  | 25520 | 1.6858          |
| 1.8386        | 81.0  | 25839 | 1.7103          |
| 1.837         | 82.0  | 26158 | 1.6797          |
| 1.837         | 83.0  | 26477 | 1.6439          |
| 1.8287        | 84.0  | 26796 | 1.6634          |
| 1.7927        | 85.0  | 27115 | 1.5949          |
| 1.7927        | 86.0  | 27434 | 1.6433          |
| 1.77          | 87.0  | 27753 | 1.6567          |
| 1.7684        | 88.0  | 28072 | 1.5484          |
| 1.7684        | 89.0  | 28391 | 1.6145          |
| 1.7423        | 90.0  | 28710 | 1.6318          |
| 1.7295        | 91.0  | 29029 | 1.5427          |
| 1.7295        | 92.0  | 29348 | 1.6212          |
| 1.7098        | 93.0  | 29667 | 1.5626          |
| 1.7098        | 94.0  | 29986 | 1.6377          |
| 1.7087        | 95.0  | 30305 | 1.5701          |
| 1.6817        | 96.0  | 30624 | 1.4842          |
| 1.6817        | 97.0  | 30943 | 1.5769          |
| 1.6732        | 98.0  | 31262 | 1.5541          |
| 1.6512        | 99.0  | 31581 | 1.5648          |
| 1.6512        | 100.0 | 31900 | 1.5420          |
| 1.642         | 101.0 | 32219 | 1.4468          |
| 1.63          | 102.0 | 32538 | 1.4316          |
| 1.63          | 103.0 | 32857 | 1.4182          |
| 1.6254        | 104.0 | 33176 | 1.5147          |
| 1.6254        | 105.0 | 33495 | 1.5259          |
| 1.5967        | 106.0 | 33814 | 1.4105          |
| 1.5842        | 107.0 | 34133 | 1.3901          |
| 1.5842        | 108.0 | 34452 | 1.4855          |
| 1.5953        | 109.0 | 34771 | 1.5122          |
| 1.5772        | 110.0 | 35090 | 1.4486          |
| 1.5772        | 111.0 | 35409 | 1.3851          |
| 1.5643        | 112.0 | 35728 | 1.4139          |
| 1.5483        | 113.0 | 36047 | 1.3437          |
| 1.5483        | 114.0 | 36366 | 1.3224          |
| 1.5393        | 115.0 | 36685 | 1.3664          |
| 1.5156        | 116.0 | 37004 | 1.3576          |
| 1.5156        | 117.0 | 37323 | 1.4949          |
| 1.5242        | 118.0 | 37642 | 1.4403          |
| 1.5242        | 119.0 | 37961 | 1.3988          |
| 1.5263        | 120.0 | 38280 | 1.3910          |
| 1.5098        | 121.0 | 38599 | 1.4632          |
| 1.5098        | 122.0 | 38918 | 1.3871          |
| 1.4922        | 123.0 | 39237 | 1.3946          |
| 1.4898        | 124.0 | 39556 | 1.4390          |
| 1.4898        | 125.0 | 39875 | 1.2734          |
| 1.4807        | 126.0 | 40194 | 1.3209          |
| 1.4693        | 127.0 | 40513 | 1.3928          |
| 1.4693        | 128.0 | 40832 | 1.4490          |
| 1.466         | 129.0 | 41151 | 1.3710          |
| 1.466         | 130.0 | 41470 | 1.3957          |
| 1.4701        | 131.0 | 41789 | 1.2389          |
| 1.4411        | 132.0 | 42108 | 1.4310          |
| 1.4411        | 133.0 | 42427 | 1.3077          |
| 1.4374        | 134.0 | 42746 | 1.3514          |
| 1.4348        | 135.0 | 43065 | 1.3392          |
| 1.4348        | 136.0 | 43384 | 1.3673          |
| 1.4252        | 137.0 | 43703 | 1.3510          |
| 1.4209        | 138.0 | 44022 | 1.3027          |
| 1.4209        | 139.0 | 44341 | 1.2340          |
| 1.4195        | 140.0 | 44660 | 1.3203          |
| 1.4195        | 141.0 | 44979 | 1.3199          |
| 1.4001        | 142.0 | 45298 | 1.3095          |
| 1.416         | 143.0 | 45617 | 1.3576          |
| 1.416         | 144.0 | 45936 | 1.2701          |
| 1.4063        | 145.0 | 46255 | 1.2958          |
| 1.397         | 146.0 | 46574 | 1.2620          |
| 1.397         | 147.0 | 46893 | 1.2759          |
| 1.3987        | 148.0 | 47212 | 1.3024          |
| 1.374         | 149.0 | 47531 | 1.2234          |
| 1.374         | 150.0 | 47850 | 1.2767          |
| 1.3783        | 151.0 | 48169 | 1.2659          |
| 1.3783        | 152.0 | 48488 | 1.2980          |
| 1.3747        | 153.0 | 48807 | 1.3125          |
| 1.3761        | 154.0 | 49126 | 1.2958          |
| 1.3761        | 155.0 | 49445 | 1.3287          |
| 1.3605        | 156.0 | 49764 | 1.2724          |
| 1.3588        | 157.0 | 50083 | 1.3302          |
| 1.3588        | 158.0 | 50402 | 1.3242          |
| 1.352         | 159.0 | 50721 | 1.2390          |
| 1.3545        | 160.0 | 51040 | 1.2435          |
| 1.3545        | 161.0 | 51359 | 1.3246          |
| 1.3501        | 162.0 | 51678 | 1.2723          |
| 1.3501        | 163.0 | 51997 | 1.2139          |
| 1.3362        | 164.0 | 52316 | 1.2435          |
| 1.3457        | 165.0 | 52635 | 1.2198          |
| 1.3457        | 166.0 | 52954 | 1.2691          |
| 1.3298        | 167.0 | 53273 | 1.2945          |
| 1.335         | 168.0 | 53592 | 1.1958          |
| 1.335         | 169.0 | 53911 | 1.2011          |
| 1.3302        | 170.0 | 54230 | 1.3094          |
| 1.3258        | 171.0 | 54549 | 1.2617          |
| 1.3258        | 172.0 | 54868 | 1.3582          |
| 1.3151        | 173.0 | 55187 | 1.2570          |
| 1.3336        | 174.0 | 55506 | 1.2160          |
| 1.3336        | 175.0 | 55825 | 1.2224          |
| 1.3131        | 176.0 | 56144 | 1.2542          |
| 1.3131        | 177.0 | 56463 | 1.2518          |
| 1.3114        | 178.0 | 56782 | 1.2280          |
| 1.3034        | 179.0 | 57101 | 1.3342          |
| 1.3034        | 180.0 | 57420 | 1.2120          |
| 1.3124        | 181.0 | 57739 | 1.2109          |
| 1.3163        | 182.0 | 58058 | 1.2482          |
| 1.3163        | 183.0 | 58377 | 1.1576          |
| 1.3023        | 184.0 | 58696 | 1.2120          |
| 1.3135        | 185.0 | 59015 | 1.2895          |
| 1.3135        | 186.0 | 59334 | 1.2604          |
| 1.3053        | 187.0 | 59653 | 1.2423          |
| 1.3053        | 188.0 | 59972 | 1.1938          |
| 1.2964        | 189.0 | 60291 | 1.2544          |
| 1.2945        | 190.0 | 60610 | 1.2014          |
| 1.2945        | 191.0 | 60929 | 1.2223          |
| 1.2971        | 192.0 | 61248 | 1.2778          |
| 1.3007        | 193.0 | 61567 | 1.2427          |
| 1.3007        | 194.0 | 61886 | 1.2390          |
| 1.2885        | 195.0 | 62205 | 1.2286          |
| 1.3103        | 196.0 | 62524 | 1.2421          |
| 1.3103        | 197.0 | 62843 | 1.2015          |
| 1.3012        | 198.0 | 63162 | 1.2083          |
| 1.3012        | 199.0 | 63481 | 1.2305          |
| 1.2907        | 200.0 | 63800 | 1.1906          |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.2

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

KannadaBERT-lamb
================


This model is a fine-tuned version of Chakita/KannadaBERT on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2098


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: 2e-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: linear
* num\_epochs: 200


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: linear
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: linear
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.2