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KcELECTRA-small-v2022-finetuned-in-vehicle

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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: 0.3712
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- - Accuracy: 0.9453
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- - F1: 0.9325
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  ## Model description
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@@ -43,22 +44,42 @@ 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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- | 0.4773 | 1.0 | 33 | 0.4666 | 0.9171 | 0.8976 |
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- | 0.4439 | 2.0 | 66 | 0.4404 | 0.9204 | 0.9011 |
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- | 0.438 | 3.0 | 99 | 0.4226 | 0.9270 | 0.9119 |
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- | 0.4065 | 4.0 | 132 | 0.4094 | 0.9287 | 0.9141 |
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- | 0.3882 | 5.0 | 165 | 0.3975 | 0.9287 | 0.9151 |
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- | 0.368 | 6.0 | 198 | 0.3886 | 0.9403 | 0.9279 |
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- | 0.3531 | 7.0 | 231 | 0.3812 | 0.9436 | 0.9312 |
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- | 0.3696 | 8.0 | 264 | 0.3762 | 0.9403 | 0.9276 |
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- | 0.3542 | 9.0 | 297 | 0.3718 | 0.9453 | 0.9329 |
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- | 0.3379 | 10.0 | 330 | 0.3712 | 0.9453 | 0.9325 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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+ license: mit
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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 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: 0.5573
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+ - Accuracy: 0.8908
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+ - F1: 0.8752
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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: 30
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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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+ | 3.3864 | 1.0 | 150 | 3.3222 | 0.0992 | 0.0179 |
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+ | 3.2362 | 2.0 | 300 | 3.0647 | 0.2342 | 0.1422 |
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+ | 2.9257 | 3.0 | 450 | 2.7001 | 0.3467 | 0.2377 |
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+ | 2.5953 | 4.0 | 600 | 2.3780 | 0.46 | 0.3409 |
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+ | 2.3033 | 5.0 | 750 | 2.1041 | 0.5025 | 0.3911 |
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+ | 2.0477 | 6.0 | 900 | 1.8675 | 0.5583 | 0.4585 |
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+ | 1.8309 | 7.0 | 1050 | 1.6711 | 0.6167 | 0.5354 |
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+ | 1.6455 | 8.0 | 1200 | 1.5126 | 0.6667 | 0.5924 |
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+ | 1.4883 | 9.0 | 1350 | 1.3677 | 0.7325 | 0.6660 |
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+ | 1.3473 | 10.0 | 1500 | 1.2435 | 0.7542 | 0.6914 |
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+ | 1.23 | 11.0 | 1650 | 1.1460 | 0.7517 | 0.6911 |
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+ | 1.1299 | 12.0 | 1800 | 1.0577 | 0.7792 | 0.7251 |
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+ | 1.0437 | 13.0 | 1950 | 0.9860 | 0.7908 | 0.7397 |
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+ | 0.9683 | 14.0 | 2100 | 0.9219 | 0.8092 | 0.7625 |
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+ | 0.8973 | 15.0 | 2250 | 0.8622 | 0.8342 | 0.8028 |
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+ | 0.8417 | 16.0 | 2400 | 0.8129 | 0.8408 | 0.8122 |
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+ | 0.7891 | 17.0 | 2550 | 0.7719 | 0.85 | 0.8264 |
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+ | 0.743 | 18.0 | 2700 | 0.7323 | 0.8583 | 0.8353 |
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+ | 0.7037 | 19.0 | 2850 | 0.7005 | 0.8583 | 0.8341 |
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+ | 0.6715 | 20.0 | 3000 | 0.6696 | 0.8717 | 0.8494 |
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+ | 0.6396 | 21.0 | 3150 | 0.6469 | 0.8767 | 0.8546 |
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+ | 0.6117 | 22.0 | 3300 | 0.6285 | 0.8808 | 0.8613 |
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+ | 0.5897 | 23.0 | 3450 | 0.6137 | 0.88 | 0.8618 |
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+ | 0.5684 | 24.0 | 3600 | 0.5956 | 0.88 | 0.8604 |
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+ | 0.5522 | 25.0 | 3750 | 0.5851 | 0.8808 | 0.8622 |
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+ | 0.5429 | 26.0 | 3900 | 0.5741 | 0.8867 | 0.8697 |
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+ | 0.53 | 27.0 | 4050 | 0.5672 | 0.8892 | 0.8730 |
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+ | 0.5234 | 28.0 | 4200 | 0.5616 | 0.8892 | 0.8733 |
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+ | 0.5184 | 29.0 | 4350 | 0.5580 | 0.89 | 0.8738 |
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+ | 0.5138 | 30.0 | 4500 | 0.5573 | 0.8908 | 0.8752 |
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  ### Framework versions