ASAP_FineTuningBERT_AugV5_k4_task1_organization_fold1

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

  • Loss: 1.7186
  • Qwk: -0.1172
  • Mse: 1.7190
  • Rmse: 1.3111

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: 100

Training results

Training Loss Epoch Step Validation Loss Qwk Mse Rmse
No log 0.6667 2 10.3892 0.0021 10.3869 3.2229
No log 1.3333 4 9.2369 0.0 9.2347 3.0389
No log 2.0 6 7.6696 0.0 7.6678 2.7691
No log 2.6667 8 5.9487 0.0052 5.9473 2.4387
5.7088 3.3333 10 4.2985 0.0040 4.2975 2.0730
5.7088 4.0 12 3.1074 0.0 3.1066 1.7625
5.7088 4.6667 14 2.2112 0.1359 2.2107 1.4868
5.7088 5.3333 16 1.6103 0.0418 1.6102 1.2690
5.7088 6.0 18 1.4386 0.0171 1.4388 1.1995
2.1592 6.6667 20 1.6180 0.0211 1.6179 1.2720
2.1592 7.3333 22 1.6488 0.0211 1.6487 1.2840
2.1592 8.0 24 1.8680 0.0453 1.8677 1.3666
2.1592 8.6667 26 1.6597 0.0172 1.6595 1.2882
2.1592 9.3333 28 1.4587 0.0 1.4587 1.2078
1.794 10.0 30 1.7976 0.0448 1.7973 1.3407
1.794 10.6667 32 2.2382 0.0075 2.2378 1.4959
1.794 11.3333 34 1.8188 0.0318 1.8186 1.3486
1.794 12.0 36 1.3730 0.0106 1.3731 1.1718
1.794 12.6667 38 1.3552 0.0106 1.3553 1.1642
1.6324 13.3333 40 2.0235 -0.0185 2.0232 1.4224
1.6324 14.0 42 2.1756 -0.0312 2.1752 1.4749
1.6324 14.6667 44 1.5095 0.0171 1.5095 1.2286
1.6324 15.3333 46 1.3737 0.0 1.3738 1.1721
1.6324 16.0 48 1.6646 0.0021 1.6646 1.2902
1.4726 16.6667 50 1.5845 -0.0216 1.5845 1.2588
1.4726 17.3333 52 2.0822 -0.0899 2.0819 1.4429
1.4726 18.0 54 1.7577 -0.0849 1.7578 1.3258
1.4726 18.6667 56 2.2560 -0.1262 2.2559 1.5020
1.4726 19.3333 58 1.7144 -0.1494 1.7148 1.3095
0.8216 20.0 60 2.2461 -0.1637 2.2463 1.4988
0.8216 20.6667 62 1.9039 -0.1958 1.9044 1.3800
0.8216 21.3333 64 2.0817 -0.2083 2.0820 1.4429
0.8216 22.0 66 2.3166 -0.2342 2.3167 1.5221
0.8216 22.6667 68 1.7224 -0.2036 1.7229 1.3126
0.3453 23.3333 70 2.3052 -0.2052 2.3051 1.5183
0.3453 24.0 72 1.7551 -0.1787 1.7555 1.3249
0.3453 24.6667 74 1.6786 -0.1744 1.6789 1.2957
0.3453 25.3333 76 2.1339 -0.1743 2.1340 1.4608
0.3453 26.0 78 1.7858 -0.2332 1.7863 1.3365
0.2609 26.6667 80 2.1505 -0.2277 2.1508 1.4666
0.2609 27.3333 82 2.7219 -0.2202 2.7218 1.6498
0.2609 28.0 84 1.7849 -0.2392 1.7855 1.3362
0.2609 28.6667 86 1.7284 -0.2209 1.7290 1.3149
0.2609 29.3333 88 2.0928 -0.2024 2.0930 1.4467
0.2164 30.0 90 1.6619 -0.2043 1.6624 1.2893
0.2164 30.6667 92 1.9337 -0.1982 1.9338 1.3906
0.2164 31.3333 94 1.7289 -0.2180 1.7292 1.3150
0.2164 32.0 96 1.8669 -0.1856 1.8671 1.3664
0.2164 32.6667 98 1.9788 -0.1742 1.9790 1.4068
0.1317 33.3333 100 1.6707 -0.2031 1.6711 1.2927
0.1317 34.0 102 1.8929 -0.2023 1.8932 1.3759
0.1317 34.6667 104 1.9283 -0.1993 1.9285 1.3887
0.1317 35.3333 106 1.8071 -0.1981 1.8075 1.3444
0.1317 36.0 108 1.9996 -0.1939 1.9999 1.4142
0.1113 36.6667 110 1.8632 -0.1808 1.8635 1.3651
0.1113 37.3333 112 1.9579 -0.1915 1.9582 1.3994
0.1113 38.0 114 1.9552 -0.1971 1.9555 1.3984
0.1113 38.6667 116 1.8110 -0.2170 1.8113 1.3459
0.1113 39.3333 118 1.9609 -0.1866 1.9611 1.4004
0.1046 40.0 120 2.2146 -0.2016 2.2146 1.4882
0.1046 40.6667 122 1.9073 -0.1888 1.9075 1.3811
0.1046 41.3333 124 1.9007 -0.1854 1.9009 1.3787
0.1046 42.0 126 1.8785 -0.1839 1.8787 1.3707
0.1046 42.6667 128 1.8775 -0.1772 1.8777 1.3703
0.0893 43.3333 130 2.1369 -0.1646 2.1369 1.4618
0.0893 44.0 132 1.9042 -0.2041 1.9045 1.3800
0.0893 44.6667 134 1.5898 -0.1819 1.5904 1.2611
0.0893 45.3333 136 1.9269 -0.2040 1.9272 1.3883
0.0893 46.0 138 2.7609 -0.1838 2.7606 1.6615
0.1794 46.6667 140 2.3499 -0.1760 2.3498 1.5329
0.1794 47.3333 142 1.4866 -0.1516 1.4873 1.2195
0.1794 48.0 144 1.3650 -0.1053 1.3658 1.1687
0.1794 48.6667 146 1.5066 -0.1578 1.5071 1.2277
0.1794 49.3333 148 2.0159 -0.1617 2.0160 1.4199
0.1814 50.0 150 1.8617 -0.1742 1.8620 1.3645
0.1814 50.6667 152 1.5275 -0.1569 1.5280 1.2361
0.1814 51.3333 154 1.6315 -0.1255 1.6319 1.2774
0.1814 52.0 156 1.8713 -0.1508 1.8715 1.3680
0.1814 52.6667 158 1.6311 -0.1676 1.6315 1.2773
0.0757 53.3333 160 1.5247 -0.1346 1.5253 1.2350
0.0757 54.0 162 1.8570 -0.1479 1.8573 1.3628
0.0757 54.6667 164 1.9005 -0.1424 1.9008 1.3787
0.0757 55.3333 166 1.6320 -0.1589 1.6325 1.2777
0.0757 56.0 168 1.8147 -0.1502 1.8151 1.3473
0.0864 56.6667 170 1.7225 -0.1342 1.7230 1.3126
0.0864 57.3333 172 1.8343 -0.1362 1.8347 1.3545
0.0864 58.0 174 1.9702 -0.1382 1.9705 1.4037
0.0864 58.6667 176 1.8130 -0.1279 1.8134 1.3466
0.0864 59.3333 178 1.7278 -0.1447 1.7282 1.3146
0.0653 60.0 180 1.8177 -0.1354 1.8180 1.3483
0.0653 60.6667 182 1.7418 -0.1338 1.7422 1.3199
0.0653 61.3333 184 2.0208 -0.1401 2.0210 1.4216
0.0653 62.0 186 1.9819 -0.1382 1.9822 1.4079
0.0653 62.6667 188 1.6677 -0.1436 1.6682 1.2916
0.0743 63.3333 190 1.7414 -0.1463 1.7418 1.3198
0.0743 64.0 192 1.9924 -0.1455 1.9926 1.4116
0.0743 64.6667 194 1.7808 -0.1677 1.7812 1.3346
0.0743 65.3333 196 1.7046 -0.1344 1.7050 1.3058
0.0743 66.0 198 1.7902 -0.1548 1.7905 1.3381
0.0642 66.6667 200 1.7744 -0.1456 1.7748 1.3322
0.0642 67.3333 202 1.6760 -0.1357 1.6764 1.2948
0.0642 68.0 204 1.8329 -0.1370 1.8332 1.3540
0.0642 68.6667 206 1.7698 -0.1314 1.7701 1.3305
0.0642 69.3333 208 1.6213 -0.1505 1.6218 1.2735
0.0661 70.0 210 1.7753 -0.1294 1.7758 1.3326
0.0661 70.6667 212 1.9450 -0.1377 1.9453 1.3948
0.0661 71.3333 214 1.7475 -0.1563 1.7480 1.3221
0.0661 72.0 216 1.5722 -0.1677 1.5728 1.2541
0.0661 72.6667 218 1.6567 -0.1507 1.6573 1.2874
0.0765 73.3333 220 1.8773 -0.1324 1.8776 1.3703
0.0765 74.0 222 1.7778 -0.1474 1.7782 1.3335
0.0765 74.6667 224 1.7004 -0.1393 1.7008 1.3042
0.0765 75.3333 226 1.7185 -0.1408 1.7189 1.3111
0.0765 76.0 228 1.6860 -0.1358 1.6864 1.2986
0.0594 76.6667 230 1.7396 -0.1290 1.7399 1.3191
0.0594 77.3333 232 1.6779 -0.1291 1.6782 1.2955
0.0594 78.0 234 1.8034 -0.1175 1.8036 1.3430
0.0594 78.6667 236 1.7839 -0.1268 1.7842 1.3357
0.0594 79.3333 238 1.6662 -0.1201 1.6665 1.2909
0.0583 80.0 240 1.7429 -0.1266 1.7432 1.3203
0.0583 80.6667 242 1.8739 -0.0981 1.8741 1.3690
0.0583 81.3333 244 1.7667 -0.1247 1.7670 1.3293
0.0583 82.0 246 1.6086 -0.1281 1.6090 1.2684
0.0583 82.6667 248 1.6460 -0.1140 1.6464 1.2831
0.0481 83.3333 250 1.8204 -0.1158 1.8207 1.3493
0.0481 84.0 252 1.8508 -0.1206 1.8511 1.3605
0.0481 84.6667 254 1.7420 -0.1232 1.7424 1.3200
0.0481 85.3333 256 1.7321 -0.1268 1.7324 1.3162
0.0481 86.0 258 1.7748 -0.1264 1.7752 1.3324
0.0504 86.6667 260 1.7176 -0.1239 1.7180 1.3107
0.0504 87.3333 262 1.6264 -0.1345 1.6269 1.2755
0.0504 88.0 264 1.6239 -0.1432 1.6243 1.2745
0.0504 88.6667 266 1.7319 -0.1240 1.7323 1.3162
0.0504 89.3333 268 1.7744 -0.1151 1.7748 1.3322
0.0458 90.0 270 1.7093 -0.1168 1.7097 1.3076
0.0458 90.6667 272 1.6272 -0.1230 1.6277 1.2758
0.0458 91.3333 274 1.6418 -0.1150 1.6422 1.2815
0.0458 92.0 276 1.7312 -0.1275 1.7315 1.3159
0.0458 92.6667 278 1.8346 -0.1185 1.8349 1.3546
0.0505 93.3333 280 1.8359 -0.1185 1.8362 1.3551
0.0505 94.0 282 1.7684 -0.1200 1.7688 1.3300
0.0505 94.6667 284 1.7280 -0.1209 1.7284 1.3147
0.0505 95.3333 286 1.6758 -0.1146 1.6762 1.2947
0.0505 96.0 288 1.6664 -0.1114 1.6669 1.2911
0.0529 96.6667 290 1.6924 -0.1009 1.6928 1.3011
0.0529 97.3333 292 1.7084 -0.1168 1.7088 1.3072
0.0529 98.0 294 1.7177 -0.1182 1.7181 1.3108
0.0529 98.6667 296 1.7247 -0.1172 1.7251 1.3134
0.0529 99.3333 298 1.7214 -0.1172 1.7218 1.3122
0.0495 100.0 300 1.7186 -0.1172 1.7190 1.3111

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.19.1
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