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KcELECTRA-small-v2022-finetuned-in-vehicle

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KcELECTRA-small-v2022-finetuned-in-vehicle / README.md
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metadata
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: KcELECTRA-small-v2022-finetuned-in-vehicle
    results: []

KcELECTRA-small-v2022-finetuned-in-vehicle

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3512
  • Accuracy: 0.9267
  • F1: 0.9181

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 Accuracy F1
2.257 1.0 38 2.1802 0.41 0.2474
2.1748 2.0 76 2.0939 0.42 0.2779
2.1017 3.0 114 2.0072 0.42 0.2938
2.0129 4.0 152 1.9214 0.4267 0.3036
1.9357 5.0 190 1.8354 0.44 0.3233
1.8433 6.0 228 1.7492 0.5133 0.4003
1.7616 7.0 266 1.6635 0.5833 0.4799
1.6779 8.0 304 1.5743 0.57 0.4758
1.602 9.0 342 1.4930 0.65 0.5778
1.5121 10.0 380 1.4169 0.7 0.6336
1.4362 11.0 418 1.3440 0.7267 0.6620
1.3681 12.0 456 1.2720 0.73 0.6702
1.2934 13.0 494 1.2106 0.7333 0.6734
1.2141 14.0 532 1.1430 0.7467 0.6874
1.1599 15.0 570 1.0846 0.7533 0.6975
1.1105 16.0 608 1.0311 0.76 0.7054
1.0367 17.0 646 0.9794 0.7633 0.7120
0.9879 18.0 684 0.9321 0.7767 0.7247
0.9396 19.0 722 0.8882 0.7733 0.7204
0.8891 20.0 760 0.8504 0.7967 0.7496
0.8522 21.0 798 0.8135 0.8 0.7531
0.797 22.0 836 0.7806 0.8 0.7540
0.7532 23.0 874 0.7495 0.8233 0.7857
0.7381 24.0 912 0.7233 0.8233 0.7895
0.6876 25.0 950 0.6939 0.8333 0.8011
0.672 26.0 988 0.6655 0.8367 0.8028
0.6318 27.0 1026 0.6441 0.8433 0.8005
0.6093 28.0 1064 0.6241 0.85 0.8116
0.5908 29.0 1102 0.6047 0.8533 0.8150
0.5509 30.0 1140 0.5900 0.86 0.8244
0.5316 31.0 1178 0.5696 0.8633 0.8267
0.506 32.0 1216 0.5611 0.87 0.8433
0.4912 33.0 1254 0.5352 0.8733 0.8464
0.4707 34.0 1292 0.5234 0.8967 0.8711
0.4527 35.0 1330 0.5121 0.8933 0.8684
0.4348 36.0 1368 0.4920 0.9033 0.8848
0.3974 37.0 1406 0.4881 0.9033 0.8841
0.3817 38.0 1444 0.4744 0.91 0.8953
0.3665 39.0 1482 0.4664 0.9167 0.9040
0.3546 40.0 1520 0.4631 0.92 0.9074
0.3352 41.0 1558 0.4497 0.9167 0.9040
0.3372 42.0 1596 0.4432 0.9233 0.9113
0.3054 43.0 1634 0.4299 0.92 0.9078
0.3032 44.0 1672 0.4217 0.9233 0.9130
0.2973 45.0 1710 0.4195 0.9233 0.9133
0.2805 46.0 1748 0.4140 0.92 0.9078
0.2725 47.0 1786 0.4074 0.9233 0.9113
0.2579 48.0 1824 0.4057 0.9267 0.9146
0.2477 49.0 1862 0.4078 0.92 0.9082
0.2485 50.0 1900 0.3917 0.92 0.9089
0.24 51.0 1938 0.3942 0.92 0.9082
0.2279 52.0 1976 0.3773 0.9267 0.9169
0.2148 53.0 2014 0.3794 0.92 0.9086
0.2077 54.0 2052 0.3789 0.92 0.9082
0.2061 55.0 2090 0.3770 0.9233 0.9135
0.204 56.0 2128 0.3779 0.9267 0.9165
0.191 57.0 2166 0.3713 0.92 0.9103
0.1914 58.0 2204 0.3731 0.9233 0.9133
0.1789 59.0 2242 0.3682 0.9233 0.9132
0.1808 60.0 2280 0.3650 0.9267 0.9167
0.1677 61.0 2318 0.3603 0.9233 0.9132
0.1747 62.0 2356 0.3589 0.9233 0.9132
0.1684 63.0 2394 0.3590 0.9167 0.9069
0.159 64.0 2432 0.3573 0.9233 0.9135
0.1535 65.0 2470 0.3618 0.92 0.9101
0.1563 66.0 2508 0.3632 0.92 0.9098
0.1415 67.0 2546 0.3543 0.9233 0.9132
0.1435 68.0 2584 0.3522 0.92 0.9103
0.1421 69.0 2622 0.3552 0.9233 0.9135
0.1388 70.0 2660 0.3558 0.93 0.9196
0.1382 71.0 2698 0.3536 0.9267 0.9182
0.1326 72.0 2736 0.3429 0.9233 0.9135
0.1303 73.0 2774 0.3466 0.9267 0.9169
0.1262 74.0 2812 0.3477 0.9233 0.9140
0.1247 75.0 2850 0.3458 0.9233 0.9140
0.1198 76.0 2888 0.3518 0.9267 0.9165
0.1175 77.0 2926 0.3517 0.9233 0.9135
0.119 78.0 2964 0.3531 0.9267 0.9181
0.1134 79.0 3002 0.3506 0.9267 0.9181
0.113 80.0 3040 0.3501 0.9233 0.9135
0.1167 81.0 3078 0.3486 0.9233 0.9135
0.1115 82.0 3116 0.3446 0.92 0.9103
0.111 83.0 3154 0.3494 0.9233 0.9135
0.107 84.0 3192 0.3504 0.9233 0.9135
0.1074 85.0 3230 0.3494 0.9233 0.9135
0.1092 86.0 3268 0.3446 0.92 0.9103
0.102 87.0 3306 0.3478 0.9233 0.9135
0.1067 88.0 3344 0.3451 0.92 0.9108
0.1073 89.0 3382 0.3477 0.9267 0.9181
0.1005 90.0 3420 0.3475 0.9233 0.9135
0.0987 91.0 3458 0.3495 0.9233 0.9135
0.1028 92.0 3496 0.3501 0.9233 0.9135
0.1027 93.0 3534 0.3498 0.9233 0.9135
0.0998 94.0 3572 0.3505 0.9233 0.9135
0.1 95.0 3610 0.3511 0.9233 0.9135
0.1013 96.0 3648 0.3509 0.9233 0.9135
0.1014 97.0 3686 0.3506 0.9267 0.9181
0.1034 98.0 3724 0.3509 0.9267 0.9181
0.0958 99.0 3762 0.3512 0.9267 0.9181
0.1029 100.0 3800 0.3512 0.9267 0.9181

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.1
  • Tokenizers 0.13.2