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KcELECTRA-small-v2022-finetuned-in-vehicle

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  ---
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- license: mit
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -15,11 +14,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # KcELECTRA-small-v2022-finetuned-in-vehicle
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- This model is a fine-tuned version of [beomi/KcELECTRA-small-v2022](https://huggingface.co/beomi/KcELECTRA-small-v2022) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.2620
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- - Accuracy: 0.4033
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- - F1: 0.2471
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  ## Model description
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@@ -44,22 +43,112 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | 2.592 | 1.0 | 38 | 2.5666 | 0.18 | 0.0549 |
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- | 2.5555 | 2.0 | 76 | 2.5247 | 0.19 | 0.0725 |
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- | 2.5195 | 3.0 | 114 | 2.4783 | 0.3233 | 0.1821 |
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- | 2.4729 | 4.0 | 152 | 2.4296 | 0.36 | 0.2079 |
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- | 2.4326 | 5.0 | 190 | 2.3825 | 0.38 | 0.2220 |
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- | 2.3876 | 6.0 | 228 | 2.3407 | 0.3967 | 0.2318 |
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- | 2.3521 | 7.0 | 266 | 2.3069 | 0.3967 | 0.2331 |
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- | 2.3246 | 8.0 | 304 | 2.2815 | 0.3967 | 0.2346 |
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- | 2.3081 | 9.0 | 342 | 2.2669 | 0.4033 | 0.2468 |
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- | 2.2913 | 10.0 | 380 | 2.2620 | 0.4033 | 0.2471 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  ---
 
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  # KcELECTRA-small-v2022-finetuned-in-vehicle
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+ This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3512
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+ - Accuracy: 0.9267
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+ - F1: 0.9181
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 100
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | 2.257 | 1.0 | 38 | 2.1802 | 0.41 | 0.2474 |
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+ | 2.1748 | 2.0 | 76 | 2.0939 | 0.42 | 0.2779 |
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+ | 2.1017 | 3.0 | 114 | 2.0072 | 0.42 | 0.2938 |
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+ | 2.0129 | 4.0 | 152 | 1.9214 | 0.4267 | 0.3036 |
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+ | 1.9357 | 5.0 | 190 | 1.8354 | 0.44 | 0.3233 |
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+ | 1.8433 | 6.0 | 228 | 1.7492 | 0.5133 | 0.4003 |
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+ | 1.7616 | 7.0 | 266 | 1.6635 | 0.5833 | 0.4799 |
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+ | 1.6779 | 8.0 | 304 | 1.5743 | 0.57 | 0.4758 |
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+ | 1.602 | 9.0 | 342 | 1.4930 | 0.65 | 0.5778 |
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+ | 1.5121 | 10.0 | 380 | 1.4169 | 0.7 | 0.6336 |
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+ | 1.4362 | 11.0 | 418 | 1.3440 | 0.7267 | 0.6620 |
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+ | 1.3681 | 12.0 | 456 | 1.2720 | 0.73 | 0.6702 |
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+ | 1.2934 | 13.0 | 494 | 1.2106 | 0.7333 | 0.6734 |
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+ | 1.2141 | 14.0 | 532 | 1.1430 | 0.7467 | 0.6874 |
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+ | 1.1599 | 15.0 | 570 | 1.0846 | 0.7533 | 0.6975 |
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+ | 1.1105 | 16.0 | 608 | 1.0311 | 0.76 | 0.7054 |
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+ | 1.0367 | 17.0 | 646 | 0.9794 | 0.7633 | 0.7120 |
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+ | 0.9879 | 18.0 | 684 | 0.9321 | 0.7767 | 0.7247 |
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+ | 0.9396 | 19.0 | 722 | 0.8882 | 0.7733 | 0.7204 |
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+ | 0.8891 | 20.0 | 760 | 0.8504 | 0.7967 | 0.7496 |
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+ | 0.8522 | 21.0 | 798 | 0.8135 | 0.8 | 0.7531 |
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+ | 0.797 | 22.0 | 836 | 0.7806 | 0.8 | 0.7540 |
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+ | 0.7532 | 23.0 | 874 | 0.7495 | 0.8233 | 0.7857 |
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+ | 0.7381 | 24.0 | 912 | 0.7233 | 0.8233 | 0.7895 |
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+ | 0.6876 | 25.0 | 950 | 0.6939 | 0.8333 | 0.8011 |
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+ | 0.672 | 26.0 | 988 | 0.6655 | 0.8367 | 0.8028 |
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+ | 0.6318 | 27.0 | 1026 | 0.6441 | 0.8433 | 0.8005 |
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+ | 0.6093 | 28.0 | 1064 | 0.6241 | 0.85 | 0.8116 |
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+ | 0.5908 | 29.0 | 1102 | 0.6047 | 0.8533 | 0.8150 |
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+ | 0.5509 | 30.0 | 1140 | 0.5900 | 0.86 | 0.8244 |
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+ | 0.5316 | 31.0 | 1178 | 0.5696 | 0.8633 | 0.8267 |
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+ | 0.506 | 32.0 | 1216 | 0.5611 | 0.87 | 0.8433 |
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+ | 0.4912 | 33.0 | 1254 | 0.5352 | 0.8733 | 0.8464 |
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+ | 0.4707 | 34.0 | 1292 | 0.5234 | 0.8967 | 0.8711 |
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+ | 0.4527 | 35.0 | 1330 | 0.5121 | 0.8933 | 0.8684 |
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+ | 0.4348 | 36.0 | 1368 | 0.4920 | 0.9033 | 0.8848 |
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+ | 0.3974 | 37.0 | 1406 | 0.4881 | 0.9033 | 0.8841 |
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+ | 0.3817 | 38.0 | 1444 | 0.4744 | 0.91 | 0.8953 |
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+ | 0.3665 | 39.0 | 1482 | 0.4664 | 0.9167 | 0.9040 |
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+ | 0.3546 | 40.0 | 1520 | 0.4631 | 0.92 | 0.9074 |
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+ | 0.3352 | 41.0 | 1558 | 0.4497 | 0.9167 | 0.9040 |
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+ | 0.3372 | 42.0 | 1596 | 0.4432 | 0.9233 | 0.9113 |
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+ | 0.3054 | 43.0 | 1634 | 0.4299 | 0.92 | 0.9078 |
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+ | 0.3032 | 44.0 | 1672 | 0.4217 | 0.9233 | 0.9130 |
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+ | 0.2973 | 45.0 | 1710 | 0.4195 | 0.9233 | 0.9133 |
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+ | 0.2805 | 46.0 | 1748 | 0.4140 | 0.92 | 0.9078 |
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+ | 0.2725 | 47.0 | 1786 | 0.4074 | 0.9233 | 0.9113 |
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+ | 0.2579 | 48.0 | 1824 | 0.4057 | 0.9267 | 0.9146 |
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+ | 0.2477 | 49.0 | 1862 | 0.4078 | 0.92 | 0.9082 |
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+ | 0.2485 | 50.0 | 1900 | 0.3917 | 0.92 | 0.9089 |
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+ | 0.24 | 51.0 | 1938 | 0.3942 | 0.92 | 0.9082 |
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+ | 0.2279 | 52.0 | 1976 | 0.3773 | 0.9267 | 0.9169 |
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+ | 0.2148 | 53.0 | 2014 | 0.3794 | 0.92 | 0.9086 |
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+ | 0.2077 | 54.0 | 2052 | 0.3789 | 0.92 | 0.9082 |
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+ | 0.2061 | 55.0 | 2090 | 0.3770 | 0.9233 | 0.9135 |
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+ | 0.204 | 56.0 | 2128 | 0.3779 | 0.9267 | 0.9165 |
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+ | 0.191 | 57.0 | 2166 | 0.3713 | 0.92 | 0.9103 |
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+ | 0.1914 | 58.0 | 2204 | 0.3731 | 0.9233 | 0.9133 |
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+ | 0.1789 | 59.0 | 2242 | 0.3682 | 0.9233 | 0.9132 |
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+ | 0.1808 | 60.0 | 2280 | 0.3650 | 0.9267 | 0.9167 |
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+ | 0.1677 | 61.0 | 2318 | 0.3603 | 0.9233 | 0.9132 |
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+ | 0.1747 | 62.0 | 2356 | 0.3589 | 0.9233 | 0.9132 |
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+ | 0.1684 | 63.0 | 2394 | 0.3590 | 0.9167 | 0.9069 |
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+ | 0.159 | 64.0 | 2432 | 0.3573 | 0.9233 | 0.9135 |
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+ | 0.1535 | 65.0 | 2470 | 0.3618 | 0.92 | 0.9101 |
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+ | 0.1563 | 66.0 | 2508 | 0.3632 | 0.92 | 0.9098 |
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+ | 0.1415 | 67.0 | 2546 | 0.3543 | 0.9233 | 0.9132 |
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+ | 0.1435 | 68.0 | 2584 | 0.3522 | 0.92 | 0.9103 |
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+ | 0.1421 | 69.0 | 2622 | 0.3552 | 0.9233 | 0.9135 |
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+ | 0.1388 | 70.0 | 2660 | 0.3558 | 0.93 | 0.9196 |
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+ | 0.1382 | 71.0 | 2698 | 0.3536 | 0.9267 | 0.9182 |
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+ | 0.1326 | 72.0 | 2736 | 0.3429 | 0.9233 | 0.9135 |
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+ | 0.1303 | 73.0 | 2774 | 0.3466 | 0.9267 | 0.9169 |
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+ | 0.1262 | 74.0 | 2812 | 0.3477 | 0.9233 | 0.9140 |
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+ | 0.1247 | 75.0 | 2850 | 0.3458 | 0.9233 | 0.9140 |
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+ | 0.1198 | 76.0 | 2888 | 0.3518 | 0.9267 | 0.9165 |
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+ | 0.1175 | 77.0 | 2926 | 0.3517 | 0.9233 | 0.9135 |
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+ | 0.119 | 78.0 | 2964 | 0.3531 | 0.9267 | 0.9181 |
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+ | 0.1134 | 79.0 | 3002 | 0.3506 | 0.9267 | 0.9181 |
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+ | 0.113 | 80.0 | 3040 | 0.3501 | 0.9233 | 0.9135 |
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+ | 0.1167 | 81.0 | 3078 | 0.3486 | 0.9233 | 0.9135 |
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+ | 0.1115 | 82.0 | 3116 | 0.3446 | 0.92 | 0.9103 |
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+ | 0.111 | 83.0 | 3154 | 0.3494 | 0.9233 | 0.9135 |
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+ | 0.107 | 84.0 | 3192 | 0.3504 | 0.9233 | 0.9135 |
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+ | 0.1074 | 85.0 | 3230 | 0.3494 | 0.9233 | 0.9135 |
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+ | 0.1092 | 86.0 | 3268 | 0.3446 | 0.92 | 0.9103 |
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+ | 0.102 | 87.0 | 3306 | 0.3478 | 0.9233 | 0.9135 |
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+ | 0.1067 | 88.0 | 3344 | 0.3451 | 0.92 | 0.9108 |
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+ | 0.1073 | 89.0 | 3382 | 0.3477 | 0.9267 | 0.9181 |
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+ | 0.1005 | 90.0 | 3420 | 0.3475 | 0.9233 | 0.9135 |
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+ | 0.0987 | 91.0 | 3458 | 0.3495 | 0.9233 | 0.9135 |
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+ | 0.1028 | 92.0 | 3496 | 0.3501 | 0.9233 | 0.9135 |
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+ | 0.1027 | 93.0 | 3534 | 0.3498 | 0.9233 | 0.9135 |
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+ | 0.0998 | 94.0 | 3572 | 0.3505 | 0.9233 | 0.9135 |
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+ | 0.1 | 95.0 | 3610 | 0.3511 | 0.9233 | 0.9135 |
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+ | 0.1013 | 96.0 | 3648 | 0.3509 | 0.9233 | 0.9135 |
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+ | 0.1014 | 97.0 | 3686 | 0.3506 | 0.9267 | 0.9181 |
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+ | 0.1034 | 98.0 | 3724 | 0.3509 | 0.9267 | 0.9181 |
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+ | 0.0958 | 99.0 | 3762 | 0.3512 | 0.9267 | 0.9181 |
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+ | 0.1029 | 100.0 | 3800 | 0.3512 | 0.9267 | 0.9181 |
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  ### Framework versions