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# distilbert-base-uncased-finetuned-diabetes_sentences

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5278
- Accuracy: 0.8462
- F1: 0.8441

## 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: 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: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1171        | 1.0   | 2    | 1.1097          | 0.4103   | 0.3239 |
| 1.0594        | 2.0   | 4    | 1.0910          | 0.5641   | 0.4791 |
| 1.0633        | 3.0   | 6    | 1.0726          | 0.5897   | 0.4859 |
| 1.0348        | 4.0   | 8    | 1.0520          | 0.6410   | 0.5779 |
| 0.9992        | 5.0   | 10   | 1.0326          | 0.5385   | 0.4980 |
| 0.9915        | 6.0   | 12   | 1.0260          | 0.4872   | 0.4518 |
| 0.9447        | 7.0   | 14   | 0.9811          | 0.5641   | 0.5369 |
| 0.8217        | 8.0   | 16   | 0.9087          | 0.8205   | 0.8205 |
| 0.8067        | 9.0   | 18   | 0.8497          | 0.8462   | 0.8437 |
| 0.7156        | 10.0  | 20   | 0.8001          | 0.8462   | 0.8437 |
| 0.6859        | 11.0  | 22   | 0.7691          | 0.8462   | 0.8437 |
| 0.5988        | 12.0  | 24   | 0.7399          | 0.8462   | 0.8437 |
| 0.5365        | 13.0  | 26   | 0.6851          | 0.8462   | 0.8437 |
| 0.4467        | 14.0  | 28   | 0.6255          | 0.8462   | 0.8437 |
| 0.4347        | 15.0  | 30   | 0.5791          | 0.8462   | 0.8437 |
| 0.363         | 16.0  | 32   | 0.5482          | 0.8462   | 0.8437 |
| 0.2946        | 17.0  | 34   | 0.5359          | 0.7949   | 0.7967 |
| 0.2343        | 18.0  | 36   | 0.4981          | 0.7949   | 0.7967 |
| 0.1999        | 19.0  | 38   | 0.4467          | 0.8718   | 0.8706 |
| 0.1615        | 20.0  | 40   | 0.4282          | 0.8718   | 0.8706 |
| 0.1314        | 21.0  | 42   | 0.4236          | 0.8718   | 0.8706 |
| 0.1386        | 22.0  | 44   | 0.4183          | 0.8718   | 0.8706 |
| 0.0973        | 23.0  | 46   | 0.4291          | 0.8462   | 0.8467 |
| 0.0853        | 24.0  | 48   | 0.4173          | 0.8462   | 0.8467 |
| 0.0732        | 25.0  | 50   | 0.3749          | 0.8462   | 0.8467 |
| 0.0641        | 26.0  | 52   | 0.3341          | 0.8974   | 0.8971 |
| 0.0541        | 27.0  | 54   | 0.3223          | 0.8974   | 0.8971 |
| 0.0481        | 28.0  | 56   | 0.3277          | 0.8974   | 0.8971 |
| 0.0383        | 29.0  | 58   | 0.3415          | 0.8974   | 0.8971 |
| 0.036         | 30.0  | 60   | 0.3609          | 0.8974   | 0.8971 |
| 0.0299        | 31.0  | 62   | 0.3823          | 0.8974   | 0.8971 |
| 0.0321        | 32.0  | 64   | 0.4026          | 0.8974   | 0.8971 |
| 0.03          | 33.0  | 66   | 0.4176          | 0.8718   | 0.8706 |
| 0.0277        | 34.0  | 68   | 0.4201          | 0.8718   | 0.8706 |
| 0.0236        | 35.0  | 70   | 0.4129          | 0.8718   | 0.8706 |
| 0.022         | 36.0  | 72   | 0.4003          | 0.8974   | 0.8971 |
| 0.022         | 37.0  | 74   | 0.3865          | 0.8974   | 0.8971 |
| 0.0211        | 38.0  | 76   | 0.3731          | 0.8974   | 0.8971 |
| 0.017         | 39.0  | 78   | 0.3634          | 0.8718   | 0.8705 |
| 0.0188        | 40.0  | 80   | 0.3618          | 0.8718   | 0.8705 |
| 0.0169        | 41.0  | 82   | 0.3683          | 0.8718   | 0.8705 |
| 0.0161        | 42.0  | 84   | 0.3810          | 0.8718   | 0.8705 |
| 0.0162        | 43.0  | 86   | 0.3944          | 0.8718   | 0.8705 |
| 0.0141        | 44.0  | 88   | 0.4091          | 0.8974   | 0.8971 |
| 0.0132        | 45.0  | 90   | 0.4233          | 0.8974   | 0.8971 |
| 0.0143        | 46.0  | 92   | 0.4335          | 0.8718   | 0.8706 |
| 0.0142        | 47.0  | 94   | 0.4413          | 0.8718   | 0.8706 |
| 0.0125        | 48.0  | 96   | 0.4436          | 0.8718   | 0.8706 |
| 0.0115        | 49.0  | 98   | 0.4437          | 0.8718   | 0.8706 |
| 0.0106        | 50.0  | 100  | 0.4410          | 0.8462   | 0.8441 |
| 0.0109        | 51.0  | 102  | 0.4376          | 0.8462   | 0.8441 |
| 0.0119        | 52.0  | 104  | 0.4341          | 0.8462   | 0.8441 |
| 0.012         | 53.0  | 106  | 0.4322          | 0.8718   | 0.8705 |
| 0.0122        | 54.0  | 108  | 0.4314          | 0.8718   | 0.8705 |
| 0.0107        | 55.0  | 110  | 0.4315          | 0.8718   | 0.8705 |
| 0.0102        | 56.0  | 112  | 0.4324          | 0.8718   | 0.8705 |
| 0.0102        | 57.0  | 114  | 0.4351          | 0.8462   | 0.8441 |
| 0.0098        | 58.0  | 116  | 0.4379          | 0.8462   | 0.8441 |
| 0.009         | 59.0  | 118  | 0.4399          | 0.8462   | 0.8441 |
| 0.0099        | 60.0  | 120  | 0.4415          | 0.8462   | 0.8441 |
| 0.0094        | 61.0  | 122  | 0.4429          | 0.8462   | 0.8441 |
| 0.008         | 62.0  | 124  | 0.4479          | 0.8462   | 0.8441 |
| 0.0084        | 63.0  | 126  | 0.4531          | 0.8462   | 0.8441 |
| 0.0079        | 64.0  | 128  | 0.4571          | 0.8462   | 0.8441 |
| 0.0079        | 65.0  | 130  | 0.4607          | 0.8462   | 0.8441 |
| 0.0076        | 66.0  | 132  | 0.4637          | 0.8462   | 0.8441 |
| 0.0072        | 67.0  | 134  | 0.4659          | 0.8462   | 0.8441 |
| 0.0076        | 68.0  | 136  | 0.4693          | 0.8462   | 0.8441 |
| 0.0078        | 69.0  | 138  | 0.4726          | 0.8462   | 0.8441 |
| 0.0066        | 70.0  | 140  | 0.4729          | 0.8462   | 0.8441 |
| 0.0082        | 71.0  | 142  | 0.4711          | 0.8462   | 0.8441 |
| 0.0075        | 72.0  | 144  | 0.4673          | 0.8462   | 0.8441 |
| 0.0065        | 73.0  | 146  | 0.4645          | 0.8462   | 0.8441 |
| 0.0064        | 74.0  | 148  | 0.4623          | 0.8462   | 0.8441 |
| 0.0075        | 75.0  | 150  | 0.4613          | 0.8718   | 0.8705 |
| 0.0064        | 76.0  | 152  | 0.4616          | 0.8718   | 0.8705 |
| 0.0063        | 77.0  | 154  | 0.4627          | 0.8462   | 0.8441 |
| 0.0072        | 78.0  | 156  | 0.4635          | 0.8462   | 0.8441 |
| 0.0058        | 79.0  | 158  | 0.4636          | 0.8462   | 0.8441 |
| 0.006         | 80.0  | 160  | 0.4641          | 0.8462   | 0.8441 |
| 0.0061        | 81.0  | 162  | 0.4651          | 0.8462   | 0.8441 |
| 0.0054        | 82.0  | 164  | 0.4675          | 0.8462   | 0.8441 |
| 0.0066        | 83.0  | 166  | 0.4692          | 0.8462   | 0.8441 |
| 0.0056        | 84.0  | 168  | 0.4699          | 0.8462   | 0.8441 |
| 0.0058        | 85.0  | 170  | 0.4706          | 0.8462   | 0.8441 |
| 0.0056        | 86.0  | 172  | 0.4718          | 0.8462   | 0.8441 |
| 0.005         | 87.0  | 174  | 0.4745          | 0.8462   | 0.8441 |
| 0.0062        | 88.0  | 176  | 0.4766          | 0.8462   | 0.8441 |
| 0.0052        | 89.0  | 178  | 0.4786          | 0.8462   | 0.8441 |
| 0.0055        | 90.0  | 180  | 0.4801          | 0.8462   | 0.8441 |
| 0.0052        | 91.0  | 182  | 0.4811          | 0.8462   | 0.8441 |
| 0.0052        | 92.0  | 184  | 0.4818          | 0.8462   | 0.8441 |
| 0.0057        | 93.0  | 186  | 0.4832          | 0.8462   | 0.8441 |
| 0.005         | 94.0  | 188  | 0.4844          | 0.8462   | 0.8441 |
| 0.0055        | 95.0  | 190  | 0.4850          | 0.8462   | 0.8441 |
| 0.005         | 96.0  | 192  | 0.4852          | 0.8462   | 0.8441 |
| 0.0055        | 97.0  | 194  | 0.4860          | 0.8462   | 0.8441 |
| 0.0047        | 98.0  | 196  | 0.4872          | 0.8462   | 0.8441 |
| 0.0043        | 99.0  | 198  | 0.4889          | 0.8462   | 0.8441 |
| 0.0049        | 100.0 | 200  | 0.4902          | 0.8462   | 0.8441 |
| 0.0048        | 101.0 | 202  | 0.4909          | 0.8462   | 0.8441 |
| 0.0044        | 102.0 | 204  | 0.4908          | 0.8462   | 0.8441 |
| 0.004         | 103.0 | 206  | 0.4915          | 0.8462   | 0.8441 |
| 0.0044        | 104.0 | 208  | 0.4918          | 0.8462   | 0.8441 |
| 0.0044        | 105.0 | 210  | 0.4935          | 0.8462   | 0.8441 |
| 0.0043        | 106.0 | 212  | 0.4956          | 0.8462   | 0.8441 |
| 0.004         | 107.0 | 214  | 0.4978          | 0.8462   | 0.8441 |
| 0.0047        | 108.0 | 216  | 0.4987          | 0.8462   | 0.8441 |
| 0.0037        | 109.0 | 218  | 0.4994          | 0.8462   | 0.8441 |
| 0.0046        | 110.0 | 220  | 0.5012          | 0.8462   | 0.8441 |
| 0.004         | 111.0 | 222  | 0.5021          | 0.8462   | 0.8441 |
| 0.004         | 112.0 | 224  | 0.5030          | 0.8462   | 0.8441 |
| 0.004         | 113.0 | 226  | 0.5044          | 0.8462   | 0.8441 |
| 0.0039        | 114.0 | 228  | 0.5053          | 0.8462   | 0.8441 |
| 0.0038        | 115.0 | 230  | 0.5058          | 0.8462   | 0.8441 |
| 0.0041        | 116.0 | 232  | 0.5054          | 0.8462   | 0.8441 |
| 0.0038        | 117.0 | 234  | 0.5047          | 0.8462   | 0.8441 |
| 0.0035        | 118.0 | 236  | 0.5043          | 0.8462   | 0.8441 |
| 0.004         | 119.0 | 238  | 0.5035          | 0.8462   | 0.8441 |
| 0.0039        | 120.0 | 240  | 0.5029          | 0.8462   | 0.8441 |
| 0.0036        | 121.0 | 242  | 0.5019          | 0.8462   | 0.8441 |
| 0.0042        | 122.0 | 244  | 0.5012          | 0.8462   | 0.8441 |
| 0.0033        | 123.0 | 246  | 0.5005          | 0.8462   | 0.8441 |
| 0.0034        | 124.0 | 248  | 0.5003          | 0.8462   | 0.8441 |
| 0.0038        | 125.0 | 250  | 0.5002          | 0.8462   | 0.8441 |
| 0.0035        | 126.0 | 252  | 0.4998          | 0.8462   | 0.8441 |
| 0.0033        | 127.0 | 254  | 0.5002          | 0.8462   | 0.8441 |
| 0.0041        | 128.0 | 256  | 0.5010          | 0.8462   | 0.8441 |
| 0.0036        | 129.0 | 258  | 0.5025          | 0.8462   | 0.8441 |
| 0.0036        | 130.0 | 260  | 0.5037          | 0.8462   | 0.8441 |
| 0.0032        | 131.0 | 262  | 0.5049          | 0.8462   | 0.8441 |
| 0.0033        | 132.0 | 264  | 0.5061          | 0.8462   | 0.8441 |
| 0.0038        | 133.0 | 266  | 0.5075          | 0.8462   | 0.8441 |
| 0.0041        | 134.0 | 268  | 0.5087          | 0.8462   | 0.8441 |
| 0.0034        | 135.0 | 270  | 0.5094          | 0.8462   | 0.8441 |
| 0.0032        | 136.0 | 272  | 0.5107          | 0.8462   | 0.8441 |
| 0.0035        | 137.0 | 274  | 0.5123          | 0.8462   | 0.8441 |
| 0.0032        | 138.0 | 276  | 0.5138          | 0.8462   | 0.8441 |
| 0.0031        | 139.0 | 278  | 0.5143          | 0.8462   | 0.8441 |
| 0.0034        | 140.0 | 280  | 0.5145          | 0.8462   | 0.8441 |
| 0.0036        | 141.0 | 282  | 0.5151          | 0.8462   | 0.8441 |
| 0.003         | 142.0 | 284  | 0.5160          | 0.8462   | 0.8441 |
| 0.0034        | 143.0 | 286  | 0.5162          | 0.8462   | 0.8441 |
| 0.0031        | 144.0 | 288  | 0.5160          | 0.8462   | 0.8441 |
| 0.0031        | 145.0 | 290  | 0.5157          | 0.8462   | 0.8441 |
| 0.0032        | 146.0 | 292  | 0.5155          | 0.8462   | 0.8441 |
| 0.0029        | 147.0 | 294  | 0.5159          | 0.8462   | 0.8441 |
| 0.0032        | 148.0 | 296  | 0.5162          | 0.8462   | 0.8441 |
| 0.0036        | 149.0 | 298  | 0.5164          | 0.8462   | 0.8441 |
| 0.0028        | 150.0 | 300  | 0.5167          | 0.8462   | 0.8441 |
| 0.0026        | 151.0 | 302  | 0.5172          | 0.8462   | 0.8441 |
| 0.0028        | 152.0 | 304  | 0.5174          | 0.8462   | 0.8441 |
| 0.0031        | 153.0 | 306  | 0.5172          | 0.8462   | 0.8441 |
| 0.0029        | 154.0 | 308  | 0.5168          | 0.8462   | 0.8441 |
| 0.0031        | 155.0 | 310  | 0.5168          | 0.8462   | 0.8441 |
| 0.0033        | 156.0 | 312  | 0.5167          | 0.8462   | 0.8441 |
| 0.003         | 157.0 | 314  | 0.5168          | 0.8462   | 0.8441 |
| 0.0029        | 158.0 | 316  | 0.5175          | 0.8462   | 0.8441 |
| 0.0031        | 159.0 | 318  | 0.5181          | 0.8462   | 0.8441 |
| 0.003         | 160.0 | 320  | 0.5186          | 0.8462   | 0.8441 |
| 0.0031        | 161.0 | 322  | 0.5190          | 0.8462   | 0.8441 |
| 0.0032        | 162.0 | 324  | 0.5194          | 0.8462   | 0.8441 |
| 0.0028        | 163.0 | 326  | 0.5201          | 0.8462   | 0.8441 |
| 0.0026        | 164.0 | 328  | 0.5209          | 0.8462   | 0.8441 |
| 0.0032        | 165.0 | 330  | 0.5218          | 0.8462   | 0.8441 |
| 0.0031        | 166.0 | 332  | 0.5226          | 0.8462   | 0.8441 |
| 0.0029        | 167.0 | 334  | 0.5234          | 0.8462   | 0.8441 |
| 0.0032        | 168.0 | 336  | 0.5239          | 0.8462   | 0.8441 |
| 0.0031        | 169.0 | 338  | 0.5240          | 0.8462   | 0.8441 |
| 0.003         | 170.0 | 340  | 0.5243          | 0.8462   | 0.8441 |
| 0.0031        | 171.0 | 342  | 0.5246          | 0.8462   | 0.8441 |
| 0.0024        | 172.0 | 344  | 0.5250          | 0.8462   | 0.8441 |
| 0.0025        | 173.0 | 346  | 0.5256          | 0.8462   | 0.8441 |
| 0.0028        | 174.0 | 348  | 0.5265          | 0.8462   | 0.8441 |
| 0.003         | 175.0 | 350  | 0.5272          | 0.8462   | 0.8441 |
| 0.003         | 176.0 | 352  | 0.5275          | 0.8462   | 0.8441 |
| 0.0027        | 177.0 | 354  | 0.5278          | 0.8462   | 0.8441 |
| 0.0027        | 178.0 | 356  | 0.5277          | 0.8462   | 0.8441 |
| 0.0028        | 179.0 | 358  | 0.5276          | 0.8462   | 0.8441 |
| 0.0027        | 180.0 | 360  | 0.5274          | 0.8462   | 0.8441 |
| 0.0028        | 181.0 | 362  | 0.5272          | 0.8462   | 0.8441 |
| 0.0035        | 182.0 | 364  | 0.5270          | 0.8462   | 0.8441 |
| 0.003         | 183.0 | 366  | 0.5269          | 0.8462   | 0.8441 |
| 0.0028        | 184.0 | 368  | 0.5267          | 0.8462   | 0.8441 |
| 0.0026        | 185.0 | 370  | 0.5266          | 0.8462   | 0.8441 |
| 0.0033        | 186.0 | 372  | 0.5265          | 0.8462   | 0.8441 |
| 0.0028        | 187.0 | 374  | 0.5265          | 0.8462   | 0.8441 |
| 0.0025        | 188.0 | 376  | 0.5267          | 0.8462   | 0.8441 |
| 0.0029        | 189.0 | 378  | 0.5268          | 0.8462   | 0.8441 |
| 0.0029        | 190.0 | 380  | 0.5269          | 0.8462   | 0.8441 |
| 0.0024        | 191.0 | 382  | 0.5270          | 0.8462   | 0.8441 |
| 0.0031        | 192.0 | 384  | 0.5271          | 0.8462   | 0.8441 |
| 0.0028        | 193.0 | 386  | 0.5273          | 0.8462   | 0.8441 |
| 0.0026        | 194.0 | 388  | 0.5274          | 0.8462   | 0.8441 |
| 0.0027        | 195.0 | 390  | 0.5275          | 0.8462   | 0.8441 |
| 0.0026        | 196.0 | 392  | 0.5276          | 0.8462   | 0.8441 |
| 0.0026        | 197.0 | 394  | 0.5277          | 0.8462   | 0.8441 |
| 0.0028        | 198.0 | 396  | 0.5277          | 0.8462   | 0.8441 |
| 0.0026        | 199.0 | 398  | 0.5278          | 0.8462   | 0.8441 |
| 0.003         | 200.0 | 400  | 0.5278          | 0.8462   | 0.8441 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-diabetes_sentences", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

distilbert-base-uncased-finetuned-diabetes\_sentences
=====================================================


This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5278
* Accuracy: 0.8462
* F1: 0.8441


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: 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: 200


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3