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distilbert-base-uncased-finetuned-diabetes_sentences

Text Classification
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PyTorch
TensorBoard
distilbert
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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

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
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Evaluation results

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