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End of training

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README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2535
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- - Accuracy: 0.9132
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- - F1: 0.9412
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- - Precision: 0.9286
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- - Recall: 0.9542
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  ## Model description
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@@ -49,22 +49,25 @@ 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 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.3084 | 1.0 | 795 | 0.2535 | 0.9132 | 0.9412 | 0.9286 | 0.9542 |
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- | 0.2129 | 2.0 | 1590 | 0.2975 | 0.9056 | 0.9369 | 0.9131 | 0.9620 |
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- | 0.1516 | 3.0 | 2385 | 0.3605 | 0.9043 | 0.9346 | 0.9314 | 0.9378 |
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- | 0.095 | 4.0 | 3180 | 0.5394 | 0.8943 | 0.9301 | 0.8973 | 0.9655 |
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- | 0.076 | 5.0 | 3975 | 0.5923 | 0.8955 | 0.9292 | 0.9182 | 0.9404 |
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- | 0.0399 | 6.0 | 4770 | 0.5995 | 0.8899 | 0.9247 | 0.9212 | 0.9283 |
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- | 0.0288 | 7.0 | 5565 | 0.7001 | 0.8930 | 0.9261 | 0.9326 | 0.9197 |
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- | 0.0178 | 8.0 | 6360 | 0.7846 | 0.8930 | 0.9285 | 0.9049 | 0.9534 |
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- | 0.0083 | 9.0 | 7155 | 0.7989 | 0.8943 | 0.9288 | 0.9125 | 0.9456 |
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- | 0.0063 | 10.0 | 7950 | 0.8204 | 0.8924 | 0.9276 | 0.9102 | 0.9456 |
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2986
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+ - Accuracy: 0.9119
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+ - F1: 0.8872
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+ - Precision: 0.8921
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+ - Recall: 0.8827
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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: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | No log | 0.06 | 50 | 0.3626 | 0.8748 | 0.8410 | 0.8423 | 0.8398 |
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+ | No log | 0.13 | 100 | 0.3231 | 0.8962 | 0.8699 | 0.8666 | 0.8734 |
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+ | No log | 0.19 | 150 | 0.4256 | 0.8974 | 0.8626 | 0.8892 | 0.8437 |
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+ | No log | 0.25 | 200 | 0.3339 | 0.9031 | 0.8744 | 0.8845 | 0.8658 |
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+ | No log | 0.31 | 250 | 0.3043 | 0.8823 | 0.8587 | 0.8446 | 0.8792 |
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+ | No log | 0.38 | 300 | 0.3125 | 0.9056 | 0.8808 | 0.8802 | 0.8813 |
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+ | No log | 0.44 | 350 | 0.2946 | 0.9075 | 0.8838 | 0.8813 | 0.8863 |
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+ | No log | 0.5 | 400 | 0.2924 | 0.9125 | 0.8898 | 0.8884 | 0.8912 |
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+ | No log | 0.57 | 450 | 0.2991 | 0.8855 | 0.8632 | 0.8480 | 0.8865 |
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+ | 0.3562 | 0.63 | 500 | 0.2986 | 0.9119 | 0.8872 | 0.8921 | 0.8827 |
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+ | 0.3562 | 0.69 | 550 | 0.2851 | 0.8779 | 0.8564 | 0.8395 | 0.8864 |
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+ | 0.3562 | 0.75 | 600 | 0.3272 | 0.9125 | 0.8868 | 0.8968 | 0.8781 |
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+ | 0.3562 | 0.82 | 650 | 0.3438 | 0.8987 | 0.8636 | 0.8933 | 0.8431 |
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  ### Framework versions
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