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calculator_model_test

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calculator_model_test

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

  • Loss: 0.0317

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: 0.001
  • train_batch_size: 512
  • eval_batch_size: 512
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 200
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
3.6147 1.0 6 3.0415
2.585 2.0 12 2.0622
1.899 3.0 18 1.6836
1.6095 4.0 24 1.6248
1.5731 5.0 30 1.5728
1.5407 6.0 36 1.5594
1.5353 7.0 42 1.5163
1.4873 8.0 48 1.4470
1.4322 9.0 54 1.4274
1.3743 10.0 60 1.3462
1.3009 11.0 66 1.2124
1.1918 12.0 72 1.1226
1.1449 13.0 78 1.1215
1.0914 14.0 84 1.0471
1.0285 15.0 90 0.9795
1.0093 16.0 96 1.0062
0.9957 17.0 102 0.9296
0.944 18.0 108 0.9268
0.9194 19.0 114 0.9417
0.9014 20.0 120 0.8415
0.8789 21.0 126 0.7764
0.8155 22.0 132 0.7639
0.8593 23.0 138 0.8127
0.836 24.0 144 0.7397
0.7613 25.0 150 0.7067
0.7818 26.0 156 0.7217
0.7702 27.0 162 0.7128
0.7376 28.0 168 0.7242
0.8006 29.0 174 0.7117
0.7561 30.0 180 0.6767
0.7185 31.0 186 0.6828
0.7055 32.0 192 0.6215
0.6967 33.0 198 0.6766
0.7193 34.0 204 0.6238
0.6791 35.0 210 0.5900
0.6741 36.0 216 0.6307
0.663 37.0 222 0.6012
0.6326 38.0 228 0.5944
0.6041 39.0 234 0.5459
0.617 40.0 240 0.5786
0.6369 41.0 246 0.5896
0.6243 42.0 252 0.5446
0.5921 43.0 258 0.4864
0.5529 44.0 264 0.5561
0.5757 45.0 270 0.5783
0.5919 46.0 276 0.5235
0.5509 47.0 282 0.4525
0.5229 48.0 288 0.5007
0.5871 49.0 294 0.5009
0.5793 50.0 300 0.5431
0.5922 51.0 306 0.5404
0.5539 52.0 312 0.5386
0.5785 53.0 318 0.4697
0.5528 54.0 324 0.5061
0.5047 55.0 330 0.4249
0.475 56.0 336 0.4206
0.5236 57.0 342 0.5689
0.576 58.0 348 0.4258
0.4862 59.0 354 0.4070
0.4946 60.0 360 0.4136
0.4527 61.0 366 0.3848
0.4522 62.0 372 0.4288
0.5087 63.0 378 0.5660
0.5559 64.0 384 0.5371
0.5153 65.0 390 0.4595
0.4503 66.0 396 0.3648
0.4191 67.0 402 0.3787
0.4522 68.0 408 0.3469
0.4096 69.0 414 0.3622
0.4502 70.0 420 0.3613
0.4138 71.0 426 0.3700
0.3896 72.0 432 0.3920
0.4271 73.0 438 0.3354
0.4107 74.0 444 0.3193
0.391 75.0 450 0.3352
0.373 76.0 456 0.3818
0.4296 77.0 462 0.3238
0.3812 78.0 468 0.3337
0.3756 79.0 474 0.3105
0.3579 80.0 480 0.3433
0.4325 81.0 486 0.3103
0.356 82.0 492 0.3060
0.3467 83.0 498 0.3780
0.3922 84.0 504 0.2863
0.3457 85.0 510 0.2865
0.3755 86.0 516 0.3041
0.3319 87.0 522 0.2777
0.3359 88.0 528 0.3803
0.4192 89.0 534 0.3473
0.3941 90.0 540 0.3745
0.3991 91.0 546 0.3331
0.3489 92.0 552 0.3579
0.3352 93.0 558 0.2947
0.3202 94.0 564 0.2416
0.3339 95.0 570 0.3635
0.4108 96.0 576 0.2779
0.3827 97.0 582 0.2846
0.3559 98.0 588 0.2754
0.2985 99.0 594 0.2107
0.264 100.0 600 0.1958
0.2807 101.0 606 0.2028
0.2861 102.0 612 0.2034
0.2661 103.0 618 0.1979
0.264 104.0 624 0.2134
0.2747 105.0 630 0.1754
0.2785 106.0 636 0.2329
0.2656 107.0 642 0.1934
0.2505 108.0 648 0.2213
0.2572 109.0 654 0.2313
0.2929 110.0 660 0.2308
0.2419 111.0 666 0.1780
0.239 112.0 672 0.1694
0.2279 113.0 678 0.1580
0.2528 114.0 684 0.3002
0.3297 115.0 690 0.2676
0.3147 116.0 696 0.3287
0.2826 117.0 702 0.1475
0.2033 118.0 708 0.1359
0.1938 119.0 714 0.1592
0.2105 120.0 720 0.1696
0.2196 121.0 726 0.1532
0.2102 122.0 732 0.1157
0.2014 123.0 738 0.1835
0.2505 124.0 744 0.1851
0.2411 125.0 750 0.2881
0.2353 126.0 756 0.1911
0.2268 127.0 762 0.1874
0.2024 128.0 768 0.1613
0.2046 129.0 774 0.1938
0.199 130.0 780 0.1129
0.1703 131.0 786 0.1511
0.1924 132.0 792 0.1744
0.1854 133.0 798 0.1238
0.1632 134.0 804 0.1050
0.1589 135.0 810 0.1316
0.1787 136.0 816 0.0895
0.1658 137.0 822 0.0836
0.141 138.0 828 0.1087
0.1671 139.0 834 0.1068
0.1557 140.0 840 0.0800
0.1488 141.0 846 0.1277
0.1709 142.0 852 0.1126
0.1499 143.0 858 0.0913
0.1597 144.0 864 0.0829
0.1314 145.0 870 0.0762
0.1501 146.0 876 0.0897
0.156 147.0 882 0.0902
0.1482 148.0 888 0.0903
0.1401 149.0 894 0.0749
0.1322 150.0 900 0.0781
0.1309 151.0 906 0.0719
0.1326 152.0 912 0.0691
0.1311 153.0 918 0.0701
0.1202 154.0 924 0.0742
0.1258 155.0 930 0.0728
0.1183 156.0 936 0.0566
0.1181 157.0 942 0.0541
0.1137 158.0 948 0.0662
0.1061 159.0 954 0.0662
0.1121 160.0 960 0.0628
0.1038 161.0 966 0.0609
0.1135 162.0 972 0.0728
0.1317 163.0 978 0.0785
0.1149 164.0 984 0.0753
0.1111 165.0 990 0.0647
0.0926 166.0 996 0.0592
0.0931 167.0 1002 0.0554
0.0865 168.0 1008 0.0480
0.0881 169.0 1014 0.0498
0.0932 170.0 1020 0.0524
0.0934 171.0 1026 0.0629
0.1054 172.0 1032 0.0561
0.0933 173.0 1038 0.0422
0.0812 174.0 1044 0.0605
0.0953 175.0 1050 0.0485
0.0963 176.0 1056 0.0394
0.0731 177.0 1062 0.0378
0.0758 178.0 1068 0.0394
0.0703 179.0 1074 0.0406
0.0756 180.0 1080 0.0427
0.0812 181.0 1086 0.0538
0.0842 182.0 1092 0.0434
0.0773 183.0 1098 0.0439
0.073 184.0 1104 0.0379
0.0707 185.0 1110 0.0422
0.0749 186.0 1116 0.0420
0.0746 187.0 1122 0.0388
0.068 188.0 1128 0.0386
0.0654 189.0 1134 0.0378
0.0647 190.0 1140 0.0335
0.0629 191.0 1146 0.0402
0.0642 192.0 1152 0.0344
0.063 193.0 1158 0.0374
0.0631 194.0 1164 0.0321
0.0605 195.0 1170 0.0356
0.065 196.0 1176 0.0334
0.0591 197.0 1182 0.0321
0.0558 198.0 1188 0.0317
0.0596 199.0 1194 0.0316
0.06 200.0 1200 0.0317

Framework versions

  • Transformers 4.38.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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Evaluation results

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